Data Engineering Process Fundamentals - Data Streaming

In modern data engineering solutions, handling streaming data is very important. Businesses often need real-time insights to promptly monitor and respond to operational changes and performance trends. A data streaming pipeline facilitates the integration of real-time data into data warehouses and visualization dashboards. To achieve this integration in a data engineering solution, understanding the principles of data streaming is essential, and how technologies like Apache Kafka and Apache Spark play a key role in building efficient streaming data pipelines.

👉 Data Engineering Process Fundamentals - Data Analysis and Visualization

What is Data Streaming

Data streaming enables us to build data integration in real-time. Unlike traditional batch processing, where data is collected and processed periodically, streaming data arrives continuously by and is processed on-the-fly. This kind of integration empowers organizations to:

• React Instantly: Timely responses to events and anomalies become possible
• Predict Trends: Identify patterns and trends as they emerge
• Enhance User Experience: Provide real-time updates and personalization
• Optimize Operations: Streamline processes and resource allocation

Data Streaming Channels

Data streaming is a continuous data flow which can arrive from a channel that is usually hosted on an HTTP end-point. The type of the channel technology depends on the provider technology stack and can be any of the following:

• Web Hooks: Web hooks are like virtual messengers that notify us when something interesting happens on the web. They are HTTP callbacks triggered by specific events, such as a change in a system. To harness data from web hooks, we set up endpoints that listen for these notifications, allowing us to react instantly to changes.

• Events: Events are a fundamental concept in data streaming. They represent occurrences in a system or application, such as a user click, a sensor detecting a temperature change, or a train arrival to a station. Events can be collected and processed in real-time by using a middleware platform like Apache Kafka or RabbitMQ, providing insights into user behavior, system health, and more.

• API Integration: APIs (Application Programming Interfaces) are bridges between different software systems. Through API integration, we can fetch data from external services, social media platforms, IoT devices, or any source that exposes an API. This seamless connectivity enables us to incorporate external data into our applications and processes by scheduling calls to the API at a certain frequency.

👍 Events are used for a wide range of real-time applications, including IoT data collection, application monitoring, and user behavior tracking. Web hooks are typically employed for integrating external services, automating workflows, and receiving notifications from third-party platforms.

Scaling to Handle a Data Stream

Data streaming sources often produce small payload size with high volume of messages. This introduces scalability concerns that should be addressed with essential components like the following:

• Streaming Infrastructure: Robust streaming infrastructure is the backbone of data streaming. This includes systems like Apache Kafka, AWS Kinesis, or Azure Stream Analytics, which facilitate the ingestion, processing, and routing of data streams

• Real-Time Processing: Traditional batch processing won't cut it for data streaming. We need real-time processing frameworks like Apache Storm, or Apache Spark Streaming to handle data as it flows

• Data Storage: Storing and managing streaming data is crucial. we might use data lakes for long-term storage and databases optimized for real-time access. Cloud storage solutions offer scalability and reliability

• Analytics and Visualization: To derive meaningful insights, we need analytics tools capable of processing streaming data. Visualization platforms like PowerBI, Looker, or custom dashboards can help you make sense of the information in real time

• Monitoring and Alerts: Proactive monitoring ensures that your data streaming pipeline is healthy. Implement alerts and triggers to respond swiftly to anomalies or critical events

• Scalable Compute Resources: As data volumes grow, compute resources should be able to scale horizontally to handle increased data loads. Cloud-based solutions are often used for this purpose

Data Streaming Components

At the heart of data streaming solutions lies technologies like Apache Kafka, a distributed event streaming platform, and Apache Spark, a versatile data processing engine. Together, they form a powerful solution that ingests, processes, and analyzes streaming data at scale.

Apache Kafka

Kafka acts as the ingestion layer or message broker in the streaming pipeline. It serves as a highly durable, fault-tolerant, and scalable event streaming platform. Data producers, which can be various sources like applications, sensors, or webhooks publish events (messages) to Kafka topics. These events are typically small pieces of data containing information such as transactions, logs, or sensor readings. Let's look at a simplified overview of how Kafka works:

• Kafka organizes events into topics. A topic is a logical channel or category to which records (messages) are sent by producers and from which records are consumed by consumers. Topics serve as the central mechanism for organizing and categorizing data within Kafka. Each topic can have multiple partitions to support fail-over scenarios

  • Kafka is distributed and provides fault tolerance. If a broker (Kafka server) fails, partitions can be replicated across multiple brokers

• Kafka follows a publish-subscribe model. Producers send records to topics, and consumers subscribe to one or more topics to receive and process those records

  • A producer is a program or process responsible for publishing records to Kafka topics. Producers generate data, which is then sent to one or more topics. Each message in a topic is identified by an offset, which represents its position within the topic.

  • A consumer is a program or process that subscribes to one or more topics and processes the records within them. Consumers can read data from topics in real-time and perform various operations on it, such as analytics, storage, or forwarding to other systems

Apache Spark Structured Streaming

Apache Spark Structured Streaming is a micro-batch processing framework built on top of Apache Spark. It enables real-time data processing by ingesting data from Kafka topics in mini-batches. Here's how the process works:

👍 Apache Spark offers a unified platform for both batch and stream processing. If your application requires seamless transitions between batch and stream processing modes, Spark can be a good fit.

• Kafka Integration: Spark Streaming integrates with Kafka using the Kafka Direct API. It can consume data directly from Kafka topics, leveraging Kafka's parallelism and fault tolerance features

• Mini-Batch Processing: Spark Streaming reads data from Kafka topics in mini-batches, typically ranging from milliseconds to seconds. Each mini-batch of data is treated as a Resilient Distributed Dataset (RDD) within the Spark ecosystem

• Data Transformation: Once the data is ingested into Spark Streaming, we can apply various transformations, computations, and analytics on the mini-batches of data. Spark provides a rich set of APIs for tasks like filtering, aggregating, joining, and machine learning

• Windowed Operations: Spark Streaming allows us to perform windowed operations, such as sliding windows or tumbling windows, to analyze data within specific time intervals. This is useful for aggregating data over fixed time periods (e.g., hourly, daily) or for tracking patterns over sliding windows

• Output: After processing, the results can be stored in various destinations, such as a data lake (e.g., Hadoop HDFS), a data warehouse (e.g., BigQuery, Redshift), or other external systems. Spark provides connectors to these storage solutions for seamless data persistence

Benefits of a Kafka and Spark Integration

A Kafka and Spark integration enables us to build solutions with High Availability requirements due to the following features:

• Fault Tolerance: Kafka ensures that events are not lost even in the face of hardware failures, making it a reliable source of data

• Scalability: Kafka scales horizontally, allowing you to handle increasing data volumes by adding more Kafka brokers

• Flexibility: Spark Streaming's flexibility in data processing and windowing operations enables a wide range of real-time analytics

• End-to-End Pipeline: By combining Kafka's ingestion capabilities with Spark's processing power, you can create end-to-end data streaming pipelines that handle real-time data ingestion, processing, and storage

Supported Programming Languages

When it comes to programming language support, both Kafka and Spark allows developers to choose the language that aligns best with their skills and project requirements.

• Kafka supports multiple programming languages, including Python, C#, and Java

• Spark also support multiple programming languages like PySpark (Python), Scala, and even R for data processing tasks. Additionally, Spark allows users to work with SQL-like expressions, making it easier to perform complex data transformations and analysis

Sample Python Code for a Kafka Producer

This is a very simple implementation of a Kafka producer using Python as the programming language. This code does not consume a data feed from a provider. It only shows how a producer sends messages to a Kafka topic.

from kafka import KafkaProducer
import time

kafka_broker = "localhost:9092"

# Create a Kafka producer instance
producer = KafkaProducer(
    bootstrap_servers=kafka_broker,  # Replace with your Kafka broker's address
    value_serializer=lambda v: str(v).encode('utf-8')
)

# Sample data message (comma-delimited)
sample_message = "timestamp,station,turnstile_id,device_id,entry,exit,entry_datetime"

try:
    # continue to run until the instance is shutdown
    while True:
        # Simulate generating a new data message. This data should come from the data provider
        data_message = sample_message + f"\n{int(time.time())},StationA,123,456,10,15,'2023-07-12 08:30:00'"

        # Send the message to the Kafka topic
        producer.send('turnstile-stream', value=data_message)

        # add logging information for tracking
        print("Message sent:", data_message)
        time.sleep(1)  # Sending messages every second
except KeyboardInterrupt:
    pass
finally:
    producer.close()

This Kafka producer code initializes a producer and sends a sample message to the specified Kafka topic. Let's review each code segment:

• Create Kafka Producer Configuration:

  • Set up the Kafka producer configuration
  • Specify the Kafka broker(s) to connect to (bootstrap.servers)

• Define Kafka Topic: Define the Kafka topic to send messages (turnstile-stream in this example)

• Create a Kafka Producer:

  • Create an instance of the Kafka producer with the broker end-point
  • Use a value_serializer to encode the string message with unicode utf-8

• Define Message Contents:

  • Prepare the message to send as a CSV string with the header and value information

• Produce Messages:

  • Use the send method of the Kafka producer to send messages to the Kafka topic, turnstile-stream

• Close the Kafka Producer:

  • Always remember to close the Kafka producer when the application terminates to avoid leaving open connections on the broker

Sample Python Code for a Kafka Consumer and Spark Client

After looking at the Kafka producer code, let's take a look at how a Kafka consumer on Spark would consume and process the messages.

from pyspark.sql import SparkSession
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
from pyspark.sql.functions import window, sum

# Create a Spark session
spark = SparkSession.builder.appName("TurnstileStreamApp").getOrCreate()

# Create a StreamingContext with a batch interval of 5 seconds
ssc = StreamingContext(spark.sparkContext, 5)

kafka_broker = "localhost:9092"

# Define the Kafka broker and topic to consume from
kafkaParams = {
    "bootstrap.servers": kafka_broker,  # Replace with your Kafka broker's address
    "auto.offset.reset": "latest",
}
topics = ["turnstile-stream"]

# Create a Kafka stream
kafkaStream = KafkaUtils.createDirectStream(ssc, topics, kafkaParams)

# Parse the Kafka stream as a DataFrame
lines = kafkaStream.map(lambda x: x[1])
df = spark.read.csv(lines)

# Define a window for aggregation (4-hour window)
windowed_df = df
  .withWatermark("entry_datetime", "4 hours") \
  # 4-hour window with a 4-hour sliding interval
  .groupBy("station", window("entry_datetime", "4 hours")) 
  .agg(
    sum("entries").alias("entries"),
    sum("exits").alias("exits")
  )

# Write the aggregated data to a blob storage as compressed CSV files
query = windowed_df.writeStream\
    .outputMode("update")\
    .foreachBatch(lambda batch_df, batch_id: batch_df.write\
        .mode("overwrite")\
        .csv("gs://your-bucket-name/")  # Replace with your blob storage path
    )\
    .start()

query.awaitTermination()

This simple example shows how to write a Kafka consumer, use Spark to process and aggregate the data, and finally write a csv file to the data lake. Let’s look at each code segment for more details:

• Create a Spark Session:

  • Initialize a Spark session with the name "TurnstileStreamApp"

• Create a StreamingContext:

  • Set up a StreamingContext with a batch interval of 5 seconds. This determines how often Spark will process incoming data

• Define Kafka Broker and Topic:

  • Specify the Kafka broker's address (localhost:9092 in this example) and the topic to consume data from ("turnstile-stream")

• Create a Kafka Stream:

  • Use KafkaUtils to create a direct stream from Kafka
  • The stream will consume data from the specified Kafka topic

• Parse the Kafka Stream:

  • Extract the message values from the Kafka stream
  • Read these messages into a DataFrame (df) using Spark's CSV reader

• Define a Window for Aggregation:

  • We specify the watermark for late data using withWatermark. This ensures that any data arriving later than the specified window is still considered for aggregation
  • Create a windowed DataFrame (windowed_df) by grouping data based on "station" and a 4-hour window
  • The "entry_datetime" column is used as the timestamp for windowing
  • Aggregations are performed to calculate the sum of "entries" and "exits" within each window

• Write the Aggregated Data to a Data Lake:

  • Define a streaming query (query) to write the aggregated data to a blob storage path
  • The "update" output mode indicates that only updated results will be written
  • A function is applied to each batch of data, which specifies how to write the data
  • In this case, data is written as compressed CSV files to a data lake location
  • The awaitTermination method ensures the query continues to run and process data until manually terminated.

This Spark example processes data from Kafka, aggregates it in 4-hour windows, and it writes the results to blob storage. The code is structured to efficiently handle real-time streaming data and organize the output into folders in the data lake based on station names and time windows. In each folder, Spark will generate filenames automatically based on the default naming convention. Typically, it uses a combination of a unique identifier and partition number to create filenames. The exact format of the file name might vary depending on the Spark version and configuration. This approach is used to send the information to a data lake, so the data transformation process can pick it up and send to a data warehouse.

Alternatively, the Spark client can send the aggregated results directly in the data warehouse. The Spark client can connect to the data warehouse, so it can directly insert the information without using a data lake as an staging step.

Solution Design and Architecture

For our solution strategy, we followed the design shown below. This design helps us ensure smooth flow, efficient processing and storage of data so that it is immediately available in our data warehouse consequently, the visualization tools. Let's break down each component and explain its purpose.

Components

• Real-Time Data Source: This is an external data source, which continuously emits data as events or messages

• Message Broker Layer: Our message broker layer is the central hub for data ingestion and distribution. It consists of two vital components:

  • Kafka Broker Instance: Kafka acts as a scalable message broker and entry point for data ingestion. It efficiently collects and organizes data in topics from the source
  • Kafka Producer (Python): To bridge the gap between the data source and Kafka, we write a Python-based Kafka producer. This component is responsible for capturing data from the real-time source and forwarding it to the Kafka instance and corresponding topic

• Stream Processing Layer: The stream processing layer is where the messages from Kafka are processed, aggregated and sent to the corresponding data storage. This layer also consists of two key components:

  • Spark Instance: Apache Spark, a high-performance stream processing framework, is responsible for processing and transforming data in real-time
  • Stream Consumer (Python): In order to consume the messages from a Kafka topic, we write a Python component that acts as both a Kafka Consumer and Spark application.
    • The Kafka consumer retrieves data from the Kafka topic, ensuring that the data is processed as soon as it arrives
    • The Spark application process the messages, aggregates the data and saves the results in the data warehouse. This dual role ensures efficient data processing and storage.

• Data Warehouse: As the final destination for our processed data, the data warehouse provides a reliable and structured repository for storing the results of our real-time data processing, so visualization tools like Looker and PowerBI can display the data as soon as the dashboards are refreshed

👉 We should note that dashboards query the data from the database. For a near real-time data to be available, the dashboard data needs to be refreshed at certain intervals (e.g., minutes or hourly). To make available the real-time data to the dashboard, there needs to be a live connection (socket) between the dashboard and the streaming platform, which is done by another system component, like Redis Cache or custom service, that could push those events on a socket channel.

DevOps Support

• Containerization: In order to continue to meet our DevOps requirements, enhance scalability and manageability, and follow best enterprise level practices, we use Docker containers for all of our components. Each component, our Kafka and Spark instance as well as our two Python-based components, runs in separate Docker container. This ensures modularity, easy deployment, and resource isolation

  • This approach also enable us to use a Kubernetes cluster , a container orchestration platform that can help us manage and deploy Docker containers at scale, to run our components. We could use Minikube for local development or use a cloud-managed Kubernetes service like Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), or Azure Kubernetes Service (AKS)

• Virtual Machine (VM): Our components need to run on a VM, so we follow the same approach and create a VM instance using a Terraform script, similar to how it was done for our batch data pipeline during our planning and design phase

Advantages

Our data streaming design offers several advantages:

• Real-time Processing: Data is processed as it arrives, enabling timely insights and rapid response to changing conditions
• Scalability: The use of Kafka and Spark allows us to scale our architecture effortlessly to handle growing data volumes
• Containerization: Docker containers simplify deployment and management, making our system highly portable and maintainable
• Integration: The seamless integration of Kafka, Spark, and the Kafka consumer as a Spark client ensures data continuity and efficient processing

This data streaming strategy, powered by Kafka and Spark, empowers us to unlock real-time insights from our data streams, providing valuable information for rapid decision-making, analytics, and storage.

Summary

In today's data-driven landscape, data streaming solutions are an absolute necessity, enabling the rapid processing and analysis of vast amounts of real-time data. Technologies like Kafka and Spark play a pivotal role in empowering organizations to harness real-time insights from their data streams.

Kafka and Spark, work together seamlessly to enable real-time data processing and analytics. Kafka handles the reliable ingestion of events, while Spark Streaming provides the tools for processing, transforming, analyzing, and storing the data in a data lake or data warehouse in near real-time, allowing businesses to make decisions much at a much faster pace.

Exercise - Data Streaming with Apache Kafka

Now that we have defined the data streaming strategy, we can continue our journey and build a containerized Kafka producer that can consume real-time data sources. Let's take a look at that next.

Coming soon!

👉 Data Engineering Process Fundamentals - Data Streaming With Apache Kafka Exercise

Thanks for reading.

Send question or comment at Twitter @ozkary

👍 Originally published by ozkary.com

Data Engineering Process Fundamentals - Data Analysis and Visualization Exercise

Data analysis and visualization are fundamental to a data-driven decision-making process. To grasp the best strategy for our scenario, we delve into the data analysis and visualization phase of the process, making data models, analyzes and diagrams that allow us to tell stories from the data.

With the understanding of best practices for data analysis and visualization, we start by creating a code-based dashboard using Python, Pandas and Plotly. We then follow up by using a high-quality enterprise tool, such as Looker, to construct a low-code cloud-hosted dashboard, providing us with insights into the type of effort each method takes.

👍 This is a dashboard created with Looker. Similar dashboards can be created with PowerBI and Tableau

Once we have designed our dashboard, we can align it with our initial requirements and proceed to formulate the data analysis conclusions, thereby facilitating informed business decisions for stakeholders. However, before delving into coding, let's commence by reviewing the data analysis specifications, which provide the blueprint for our implementation effort.

Specifications

At this stage of the process, we have a clear grasp of the requirements and a deep familiarity with the data. With these insights, we can now define our specifications as outlined below:

• Identify pertinent measures such as exits and entries
• Conduct distribution analysis based on station
  • This analysis delineates geographical boundary patterns
• Conduct distribution analysis based on days of the week and time slots

By calculating the total count of passengers for arrivals and departures, we gain a holistic comprehension of passenger flow dynamics. Furthermore, we can employ distribution analysis to investigate variations across stations, days of the week, and time slots. These analyses provide essential insights for business strategy and decision-making, allowing us to identify peak travel periods, station preferences, and time-specific trends that can help us make informed decisions.

Data Analysis Requirements

In our analysis process, we can adhere to these specified requirements:

• Determine distinct time slots for morning and afternoon analysis:

  12:00 AM - 3:59 AM
  04:00 AM - 7:59 AM
  08:00 AM - 11:59 AM
  12:00 PM - 3:59 PM
  04:00 PM - 7:59 PM
  08:00 PM - 11:59 PM
  

• Examine data regarding commuter exits (arrivals) and entries (departures)
• Implement a master filter for date ranges, which exerts control over all charts
• Incorporate a secondary filter component to facilitate station selection
• Display the aggregate counts of entries and exits for the designated date range
  • Employ score card components for this purpose
• Investigate station distributions to identify the most frequented stations
  • Utilize donut charts, with the subway station name as the primary dimension
• Analyze distributions using the day of the week to unveil peak traffic days
  • Employ bar charts to visualize entries and exits per day
• Explore distributions based on time slots to uncover daily peak hours
  • Integrate bar charts to illustrate entries and exits within each time slot

Dashboard Design

In the dashboard design, we can utilize a two-column layout, positioning the exits charts in the left column and the entries charts in the right column of the dashboard. Additionally, we can incorporate a header container to encompass the filters, date range, and station name. To support multiple devices, we need a responsive layout. We should note that when using a platform like Looker, there is really no responsive layout, but we need to define different layouts for mobile and desktop.

Layout Configuration:

• Desktop 1200px by 900px
• Mobile 360px by 1980px

UI Components

For our dashboard components, we should incorporate the following:

• Date range picker
• Station name list box
• For each selected measure (exits, entries), we should employ a set of the following components:
  • Score cards for the total numbers
  • Donut charts for station distribution
  • Bar charts for day of the week distribution
  • Bar charts for time slot distribution

Review the Code - Code Centric

The dashboard layout is done using HTML for the presentation and Python to build the different HTML elements using the dash library. All the charts are generated by plotly.

# Define the layout of the app
app.layout = html.Div([
    html.H4("MTA Turnstile Data Dashboard"),

    dcc.DatePickerRange(
        id='date-range',
        start_date=data['created_dt'].min(),
        end_date=data['created_dt'].max(),
        display_format='YYYY-MM-DD'
    ),

    dbc.Row([
        dbc.Col(
            dbc.Card(
                dbc.CardBody([
                    html.P("Total Entries"),
                    html.H5(id='total-entries')
                ]),
                className='score-card'
            ),
            width=6
        ),
        dbc.Col(
            dbc.Card(
                dbc.CardBody([
                    html.P("Total Exits"),
                    html.H5(id='total-exits')
                ]),
                className='score-card'
            ),
            width=6
        )
    ], className='score-cards'),

    dbc.Row([
            dbc.Col(
                dcc.Graph(id='top-entries-stations', className='donut-chart'),
                width=6
            ),
            dbc.Col(
                dcc.Graph(id='top-exits-stations', className='donut-chart'),
                width=6
            )
    ], className='donut-charts'),

    dbc.Row([
                dbc.Col(
                    dcc.Graph(id='exits-by-day', className='bar-chart'),
                    width=6
                ),
                dbc.Col(
                    dcc.Graph(id='entries-by-day', className='bar-chart'),
                    width=6
                )
    ], className='bar-charts'),

    dbc.Row([
                dbc.Col(
                    dcc.Graph(id='exits-by-time', className='bar-chart'),
                    width=6
                ),
                dbc.Col(
                    dcc.Graph(id='entries-by-time', className='bar-chart'),
                    width=6
                )
    ], className='bar-charts')

])

The provided Python code is building a web application dashboard layout using Dash, a Python framework for creating interactive web applications. This dashboard is designed to showcase insights and visualizations derived from MTA Turnstile Data. Here's a breakdown of the main components:

• App Layout: The app.layout defines the overall structure of the dashboard using the html.Div component. It acts as a container for all the displayed components

• Title: html.H4("MTA Turnstile Data Dashboard") creates a header displaying the title of the dashboard

• Date Picker Range: The dcc.DatePickerRange component allows users to select a date range for analysis. It's a part of Dash Core Components (dcc)

• Score Cards: The dbc.Row and dbc.Col components create rows and columns for displaying score cards using dbc.Card and dbc.CardBody. These cards show metrics like "Total Entries" and "Total Exits"

• Donut Charts: Another set of dbc.Row and dbc.Col components creates columns for displaying donut charts using the dcc.Graph component. These charts visualize the distribution of top entries and exits by station

• Bar Charts: Similar to the previous sections, dbc.Row and dbc.Col components are used to create columns for displaying bar charts using the dcc.Graph component. These charts showcase the distribution of exits and entries by day of the week and time slot

• CSS Classnames: The className attribute is used to apply CSS class names to the components, allowing for custom styling using CSS

In summary, the code establishes the layout of the dashboard with distinct sections for date selection, score cards, donut charts, and bar charts. The various visualizations and metrics offer valuable insights into MTA Turnstile Data, enabling users to comprehend passenger flow patterns and trends effectively.

def update_dashboard(start_date, end_date):
    filtered_data = data[(data['created_dt'] >= start_date) & (data['created_dt'] <= end_date)]   

    total_entries = filtered_data['entries'].sum() / 1e12  # Convert to trillions
    total_exits = filtered_data['exits'].sum() / 1e12  # Convert to trillions

    measures = ['exits','entries']    
    filtered_data["created_dt"] = pd.to_datetime(filtered_data['created_dt'])  
    measures = ['exits','entries']  

    exits_chart , entries_chart = create_station_donut_chart(filtered_data)
    exits_chart_by_day ,entries_chart_by_day = create_day_bar_chart(filtered_data, measures)
    exits_chart_by_time, entries_chart_by_time = create_time_bar_chart(filtered_data, measures)

    return (
        f"{total_entries:.2f}T",
        f"{total_exits:.2f}T",
        entries_chart,
        exits_chart,
        exits_chart_by_day,
        entries_chart_by_day,
        exits_chart_by_time,
        entries_chart_by_time
    )

The update_dashboard function is responsible for updating and refreshing the dashboard. It handles the data range change event. As the user changes the date range, this function takes in the start and end dates as inputs. The function then filters the dataset, retaining only the records falling within the specified date range. Subsequently, the function calculates key metrics for the dashboard's score cards. It computes the total number of entries and exits during the filtered time period, and these values are converted to trillions for better readability.

The code proceeds to generate various visual components for the dashboard. These components include donut charts illustrating station-wise entries and exits, bar charts showcasing entries and exits by day of the week, and another set of bar charts displaying entries and exits by time slot. Each of these visualizations is created using specialized functions like create_station_donut_chart, create_day_bar_chart, and create_time_bar_chart.

Finally, the function compiles all the generated components and calculated metrics into a tuple. This tuple is then returned by the update_dashboard function, containing values like total entries, total exits, and the various charts.

def create_station_donut_chart(df: pd.DataFrame ) -> Tuple[go.Figure, go.Figure]:
    """
     creates the station distribution donut chart   
    """
    top_entries_stations = df.groupby('station_name').agg({'entries': 'sum'}).nlargest(10, 'entries')
    top_exits_stations = df.groupby('station_name').agg({'exits': 'sum'}).nlargest(10, 'exits')

    entries_chart = px.pie(top_entries_stations, names=top_entries_stations.index, values='entries',
                           title='Top 10 Stations by Entries', hole=0.3)
    exits_chart = px.pie(top_exits_stations, names=top_exits_stations.index, values='exits',
                         title='Top 10 Stations by Exits', hole=0.3)

    entries_chart.update_traces(marker=dict(colors=px.colors.qualitative.Plotly))
    exits_chart.update_traces(marker=dict(colors=px.colors.qualitative.Plotly))
    return entries_chart, exits_chart

The create_station_donut_chart function is responsible for generating donut charts to visualize the distribution of entries and exits across the top stations. It starts by selecting the top stations based on the highest entries and exits from the provided DataFrame. Using Plotly Express, the function then constructs two separate donut charts: one for the top stations by entries and another for the top stations by exits.

Each donut chart provides a graphical representation of the distribution, where each station is represented by a segment based on the number of entries or exits it recorded. The charts are presented in a visually appealing manner with a central hole for a more focused view.

def create_day_bar_chart(df: pd.DataFrame, measures: List[str]) -> Tuple[go.Figure, go.Figure]:
    """
    Creates a bar chart using the week days from the given dataframe.
    """
    measures = ['exits','entries']
    day_categories = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']   
    group_by_date = df.groupby(["created_dt"], as_index=False)[measures].sum()

    df['weekday'] = pd.Categorical(df['created_dt'].dt.strftime('%a'),
                                                 categories=day_categories,
                                                 ordered=True)        

    group_by_weekday =  df.groupby('weekday', as_index=False)[measures].sum()

    exits_chart_by_day = px.bar(group_by_weekday, x='weekday', y='exits', color='weekday',
                                title='Exits by Day of the Week', labels={'weekday': 'Day of the Week', 'exits': 'Exits'},
                                color_discrete_sequence=['green'])

    entries_chart_by_day = px.bar(group_by_weekday, x='weekday', y='entries', color='weekday',
                                  title='Entries by Day of the Week', labels={'weekday': 'Day of the Week', 'entries': 'Entries'},
                                  color_discrete_sequence=['orange'])

    # Hide the legend on the side
    exits_chart_by_day.update_layout(showlegend=False)
    entries_chart_by_day.update_layout(showlegend=False)   

    # Return the chart
    return exits_chart_by_day, entries_chart_by_day

The create_day_bar_chart function is responsible for generating bar charts that illustrate the distribution of data based on the day of the week. Due to the limitations of the date-time data type not inherently containing day information, the function maps the data to the corresponding day category.

To begin, the function calculates the sum of the specified measures (entries and exits) for each date in the DataFrame using group_by_date. Next, it creates a new column named 'weekday' that holds the abbreviated day names (Sun, Mon, Tue, etc.) by applying the strftime method to the 'created_dt' column. This column is then transformed into a categorical variable using predefined day categories, ensuring that the order of days is preserved.

Using the grouped data by 'weekday', the function constructs two separate bar charts using Plotly Express. One chart visualizes the distribution of exits by day of the week, while the other visualizes the distribution of entries by day of the week.

def create_time_bar_chart(df: pd.DataFrame, measures : List[str] ) -> Tuple[go.Figure, go.Figure]:

    """
    Creates a bar chart using the time slot category
    """

    # Define time (hr) slots
    time_slots = {
        '12:00-3:59am': (0, 3, 0),
        '04:00-7:59am': (4, 7, 1),
        '08:00-11:59am': (8, 11, 2),
         '12:00-3:59pm': (12, 15, 3),
        '04:00-7:59pm': (16, 19, 4),
        '08:00-11:59pm': (20, 23, 5)
    }

    # Add a new column 'time_slot' based on time ranges
    def categorize_time(row):
        for slot, (start, end, order) in time_slots.items():
            if start <= row.hour <= end:
                return slot

    df['time_slot'] = df['created_dt'].apply(categorize_time)
    group_by_time = df.groupby('time_slot', as_index=False)[measures].sum()

    # Sort the grouped_data DataFrame based on the sorting value
    group_by_time_sorted = group_by_time.sort_values(by=['time_slot'], key=lambda x: x.map({slot: sort_order for slot, (_, _, sort_order) in time_slots.items()}))


    exits_chart_by_time = px.bar(group_by_time_sorted, x='time_slot', y='exits', color='time_slot',
                                title='Exits by Day of the Week', labels={'time_slot': 'Time of Day', 'exits': 'Exits'},
                                color_discrete_sequence=['green'])

    entries_chart_by_time = px.bar(group_by_time_sorted, x='time_slot', y='entries', color='time_slot',
                                  title='Entries by Day of the Week', labels={'time_slot': 'Time of Day', 'entries': 'Entries'},
                                  color_discrete_sequence=['orange'])
    # Hide the legend on the side
    exits_chart_by_time.update_layout(showlegend=False)
    entries_chart_by_time.update_layout(showlegend=False)

    return exits_chart_by_time, entries_chart_by_time

The create_time_bar_chart function is responsible for generating bar charts that depict the data distribution at specific times of the day. Just as with days of the week, the function maps and labels time ranges to create a new series, enabling the creation of these charts.

First, the function defines time slots using a dictionary, where each slot corresponds to a specific time range. For each data row, a new column named 'time_slot' is added based on the time ranges defined. This is achieved by using the categorize_time function, which checks the hour of the row's timestamp and assigns it to the appropriate time slot.

The data is then grouped by 'time_slot', and the sum of the specified measures (exits and entries) is calculated for each slot. To ensure that the time slots are displayed in the correct order, the grouped data is sorted based on a sorting value derived from the time slots' dictionary.

Using the grouped and sorted data, the function constructs two bar charts using Plotly Express. One chart visualizes the distribution of exits by time of day, while the other visualizes the distribution of entries by time of day. Each bar in the chart represents the sum of exits or entries for a specific time slot.

Once the implementation of this Python dashboard is complete, we can run it and see the following dashboard load on our browser:

Requirements

These are the requirements to be able to run the Python dashboard.

👉 Clone this repo or copy the files from this folder. We could also create a GitHub CodeSpace and run this online.

• Use the analysis_data.csv file for test data
  • Use the local file for this implementation
• Install the Python dependencies
  • Type the following from the terminal

$ pip install pandas
$ pip install plotly
$ pip install dash
$ pip install dash_bootstrap_components

How to Run It

After installing the dependencies and downloading the code, we should be able to run the code from a terminal by typing:

$ python3 dashboard.py

We should note that this is a simple implementation to illustrate the amount of effort it takes to build a dashboard using code. The code uses a local CSV file. If we need to connect to the data warehouse, we need to expand this code to use an API call that is authorized to access the data warehouse. Writing Python dashboards or creating Jupyter charts, works well for small teams that are working closely together and are running experiments on the data. However, for a more enterprise solution, we should look at using a tool like Looker or PowerBI. Let's take a look at that next.

Review the Code - Low-Code

Tools like Looker and PowerBI excel in data visualization, requiring little to no coding. These tools offer a plethora of visual elements for configuring dashboards, minimizing the need for extensive coding. For instance, these platforms effortlessly handle tasks like automatically displaying the day of the week from a date-time field.

In cases where an out-of-the-box solution is lacking, we might need to supplement it with a code snippet. For instance, consider our time range requirement. Since this is quite specific to our project, we must generate a new series with our desired labels. To achieve this, we introduce a new field that corresponds to the date-time hour value. When the field is created, we are essentially implementing a function.

The provided code reads the hour value from the date-time fields and subsequently maps it to its corresponding label.

CASE 
    WHEN HOUR(created_dt) BETWEEN 0 AND 3 THEN "12:00-3:59am" 
    WHEN HOUR(created_dt) BETWEEN 4 AND 7 THEN "04:00-7:59am" 
    WHEN HOUR(created_dt) BETWEEN 8 AND 11 THEN "08:00-11:59am" 
    WHEN HOUR(created_dt) BETWEEN 12 AND 15 THEN "12:00-3:59pm" 
    WHEN HOUR(created_dt) BETWEEN 16 AND 20 THEN "04:00-7:59pm" 
    WHEN HOUR(created_dt) BETWEEN 20 AND 23 THEN "08:00-11:59pm" 
END

Requirements

The only requirement here is to sign up with Looker Studio and have access to a data warehouse or database that can serve data and is accessible from external sources.

👉 Sign-up for Looker Studio

Other Visualizations tools:

• PowerBI
• Tableau

Looker UI

Take a look at the image below. This is the Looker UI. We should familiarize ourselves with the following areas:

• Theme and Layout: Use it to configure the theme and change the layout for mobile or desktop
• Add data: Use this to add a new data source
• Add a chart: This allows us to add new charts
• Add a control: Here, we can add the date range and station name list
• Canvas: This is where we place all the components
• Setup Pane: This allows us to configure the date range, dimension, measures, and sorting settings
• Style Pane: Here, we can configure the colors and font
• Data Pane: This displays the data sources with their fields. New fields are created as functions. When we hover over a field, we can see a function (fx) icon, which indicates that we can edit the function and configure our snippet

How to Build it

Sign up for a Looker account or use another BI tool and follow these steps:

• Create a new dashboard
• Click on the "Add Data" button
• Use the connector for our data source:
  • This should allow us to configure the credentials for access
  • Select the "rpt_turnstile" view, which already includes joins with the fact_table and dimension tables
• Once the data is loaded, we can see the dimensions and measures
• Add the dashboard filters:
  • Include a date range control for the filter, using the "created_dt" field
  • Add a list control and associate it with the station name
• Proceed to add the remaining charts:
  • Ensure that all charts are associated with the date range dimension
  • This enables filtering to cascade across all the charts
• Utilize the "entries" and "exits" measures for all dashboards:
  • Integrate two scorecards for the sum of entries and exits
  • Incorporate a donut chart to display exits and entries distribution by stations
  • Incorporate two bar charts (entries and exits) and use the weekday value from the "created_dt" dimension
    • Sort them by the weekday. Use the day number (0-6), not the name (Sun-Sat). This is achieved by adding a new field with the following code and using it for sorting:

WEEKDAY(created_dt)

• Create the time slot dimension field (click "Add Field" and enter this definition):

CASE 
    WHEN HOUR(created_dt) BETWEEN 0 AND 3 THEN "12:00-3:59am" 
    WHEN HOUR(created_dt) BETWEEN 4 AND 7 THEN "04:00-7:59am" 
    WHEN HOUR(created_dt) BETWEEN 8 AND 11 THEN "08:00-11:59am" 
    WHEN HOUR(created_dt) BETWEEN 12 AND 15 THEN "12:00-3:59pm" 
    WHEN HOUR(created_dt) BETWEEN 16 AND 19 THEN "04:00-7:59pm" 
    WHEN HOUR(created_dt) BETWEEN 20 AND 23 THEN "08:00-11:59pm" 
END

• Add two bar charts (entries and exits) and use the time slot dimension:
  • Use the hour value from the "created_dt" dimension for sorting by adding a new field and using it as your sorting criteria:

HOUR(created_dt)

View the Dashboard

After following all the specification, we should be able to preview the dashboard on the browser. We can load an example, of a dashboard by clicking on the link below:

👉 View the dashboard online

👉 View the mobile dashboard online

This is a an image of the mobile dashboard.

Data Analysis Conclusions

By examining the dashboard, the following conclusions can be observed:

• Stations with the highest distribution represent the busiest locations
• The busiest time slot for both exits and entries is between 4pm and 9pm
• Every day of the week exhibits a high volume of commuters
• Businesses can choose the stations near their locations for further analysis

With these insights, strategies can be devised to optimize marketing campaigns and target users within geo-fenced areas and during specific hours of the day that are in close proximity to corresponding business locations.

Summary

We utilize our expertise in data analysis and visualization to construct charts and build them into dashboards. We adopt two distinct approaches for dashboard creation: a code-centric method and a low-code enterprise solution like Looker. After a comprehensive comparison, we deduce that the code-centric approach is optimal for small teams, whereas it might not suffice for enterprise users, especially when targeting executive stakeholders.

Lastly, as the dashboard becomes operational, we transition into the role of business analysts, deciphering insights from the data. This enables us to offer answers aligned with our original requirements.

Next

We have successfully completed our data pipeline from CSV files to our data warehouse and dashboard. Now, let's explore an advanced concept in data engineering: data streaming, which facilitates real-time data integration. This involves the continuous and timely processing of incoming data. Technologies like Apache Kafka and Apache Spark play a crucial role in enabling efficient data streaming processes. Let's take a closer look at these components next.

Coming Soon!

👉 [Data Engineering Process Fundamentals - Real-Time Data]

Thanks for reading.

Send question or comment at Twitter @ozkary

👍 Originally published by ozkary.com

Data Engineering Process Fundamentals - Data Analysis and Visualization

After completing our data warehouse design and implementation, our data pipeline should be fully operational. We can move forward with the analysis and visualization step of our process. Data analysis entails exploring, comprehending, and reshaping data to yield insights, thereby enabling stakeholders to make informed business decisions. Conversely, data visualization employs these insights to adeptly convey information via visual elements, encompassing charts and dashboards.

👉 Data Engineering Process Fundamentals - Data Warehouse and Transformation

Data analysis entails utilizing guidelines and patterns to guide the selection of appropriate analyses tailored to the specific use case. For instance, a Business Analyst (BA) focuses on examining data summations and aggregations across categorical dimensions such as date or station name. Conversely, a Manufacturing Quality Engineer (MQE) prioritizes the examination of statistical data, encompassing metrics like the mean and standard deviation.

In data visualization, we follow guidelines and design patterns to determine the appropriate chart for our data. For instance, a Business Intelligence (BI) dashboard may employ bar and pie charts to monitor sales performance in specific regions, while a Quality Control (QA) dashboard might utilize box plots, bell curves, and control charts to assess manufacturing process quality.

Data analysis and visualization are fundamental to a data-driven decision-making process. To grasp the best strategy for our scenario, we now dive deeper into this process by using a sample dataset from our data warehouse to illustrate the approach with examples.

Data Analysis

Data analysis is the practice of exploring data and understanding its meaning. It involves activities that can help us achieve a specific goal, such as identifying data dimensions and measures, as well as data analysis to identify outliers, trends, distributions, and hypothesis testing. We can accomplish these activities by writing code snippets using Python and Pandas, Visual Studio Code or Jupyter Notebooks. What's more, we can use libraries, such as Plotly, to generate some visuals to further analyze data and create prototypes.

For low-code tools, the analysis can be done using a smart and rich user interface that automatically discovers the meta-data to identify the dataset properties like dimensions and measures. With little to no-code, those tools can help us model the data, create charts and dashboards.

Data Profiling

Data profiling is the process to identify the data types, dimensions, measures, and quantitative values, which allows the analyst to understand the characteristics of the data, so we can understand how to group the information.

• Data Types: This is the type classification of the data fields. It enables us to identify categorical (text), numeric and date-time values. The date-time data type is specially important as it provides us with the ability to slice the numeric values with a date range, specific dates and times (e.g., hourly)

• Dimensions: Dimensions are textual, and categorical attributes that describe business entities. They are often discrete and used for grouping, filtering, and organizing data

• Measures: Measures are the quantitative values that are subject to calculations such as sum, average, minimum, maximum, etc. They represent the KPIs that the organization wants to track and analyze

As an example of data profiling, we can inspect the average of arrivals and departures at certain time slots. This can help us identify patterns at different times.

👉 Clone this repo or copy the files from this folder. Use Jupyter Notebook file.

import pandas as pd

# use the sample dataset in this path Step5-Analysis/analysis_data.csv
df = pd.read_csv('./analysis_data.csv', iterator=False)
df.head(10)

# Define time (hr) slots
time_slots = {
    'morning': (8, 11),
    'afternoon': (12, 15),
    'night': (16, 20)
}
# cast the date column to datetime
df["created_dt"] = pd.to_datetime(df['created_dt'])
df["exits"] = df["exits"].astype(int)
df["entries"] = df["entries"].astype(int)

# Calculate average arrivals (exits) and departures (entries) for each time slot
for slot, (start_hour, end_hour) in time_slots.items():
    slot_data = df[(df['created_dt'].dt.hour >= start_hour) & (df['created_dt'].dt.hour < end_hour)]
    avg_arrivals = slot_data['exits'].mean()
    avg_departures = slot_data['entries'].mean()
    print(f"{slot.capitalize()} - Avg Arrivals: {avg_arrivals:.2f}, Avg Departures: {avg_departures:.2f}")

# output
Morning - Avg Arrivals: 30132528.64, Avg Departures: 37834954.08
Afternoon - Avg Arrivals: 30094161.08, Avg Departures: 37482421.78
Night - Avg Arrivals: 29513309.25, Avg Departures: 36829260.66

The code calculates the average arrivals and departures for each time slot. It prints out the results for each time slot, helping us identify the patterns of commuter activity during different times of the day.

Data Cleaning and Preprocessing

Data cleaning and preprocessing is the process of finding bad data and outliers that can affect the results. Bad data could be null values or values that are not within the range of the average trend for that day. These kinds of data problems should have been identified during the data load process, but it is always a best practice to repeat this process, even when the data comes from a trusted resource.

👍Outliers are values that are notably different from the other data points in terms of magnitude or distribution. They can be either unusually high (positive outliers) or unusually low (negative outliers) in comparison to the majority of data points.

For example, we might want to look at stations where the average number of arrivals in the morning differs unusually from the average number of departures in the evening. A normal pattern is that both should be within the threshold value.

# get the departures and arrivals for each station at the morning and night time slots
df_morning_arrivals =  df[(df['created_dt'].dt.hour >= time_slots['morning'][0]) & (df['created_dt'].dt.hour < time_slots['morning'][1])]
df_night_departures = df[(df['created_dt'].dt.hour >= time_slots['night'][0] ) & (df['created_dt'].dt.hour < time_slots['night'][1])]
# Calculate the mean arrivals and departures for each station
mean_arrivals_by_station = df_morning_arrivals.groupby('station_name')['exits'].mean()
mean_departures_by_station = df_night_departures.groupby('station_name')['entries'].mean()

# Calculate the z-scores for the differences between mean arrivals and departures
z_scores = (mean_arrivals_by_station - mean_departures_by_station) / np.sqrt(mean_arrivals_by_station.var() + mean_departures_by_station.var())

# Set a z-score threshold to identify outliers
z_score_threshold = 1.95  # You can adjust this value based on your needs

# Identify stations with outliers
outlier_stations = z_scores[abs(z_scores) > z_score_threshold]

print("Stations with outliers:")
print(outlier_stations)

# output
Stations with outliers:
station_name
183 ST          -3.170777
BAYCHESTER AV   -4.340479
JACKSON AV      -4.215668
NEW LOTS         3.124990

The output shows that there is a significant difference in the number of arrivals (morning) at these stations compared to departures later in the evening. This issue could be a result of some missing data or perhaps an event that caused the difference in commuters.

Statistical Analysis

Statistical analysis focuses on applying statistical techniques in order to draw meaningful conclusions about a set of data. It involves mathematical computations, probability theory, correlation analysis, and hypothesis testing to make inferences and predictions based on the data.

An example of statistical analysis is to describe the statistics for the numeric data and plot the relationships between two measures.

# Summary statistics
measures = ['entries','exits']
dims = ['station_name']

# Filter rows for the month of July for morning and night time slots
df_morning_july = df_morning_arrivals[df_morning_arrivals['created_dt'].dt.month == 7][measures + dims]
df_night_july = df_night_departures[df_night_departures['created_dt'].dt.month == 7][measures + dims]

correlation_data = []
for station in df_morning_july['station_name'].unique():
    morning_arrival = df_morning_july[df_morning_july['station_name'] == station]['exits'].values[0]
    evening_departure = df_night_july[df_night_july['station_name'] == station]['entries'].values[0]
    correlation_data.append({'station_name': station, 'arrivals': morning_arrival, 'departures': evening_departure})

df_correlation = pd.DataFrame(correlation_data)

# Select top 10 stations with most morning arrivals
top_stations = df_correlation.groupby('station_name')['arrivals'].sum().nlargest(10).index
df_top_stations = df_correlation[df_correlation['station_name'].isin(top_stations)]

print("Summary Statistics:")
print(df_top_stations[measures].describe() / 10000)

#output
Summary Statistics:
             entries          exits
count      10.000000      10.000000
mean     3691.269728    2954.513148
std     20853.999335   18283.964419
min         0.000000       0.000000
25%        27.126200      19.537525
50%       135.898650     100.470600
75%       615.586650     445.015200
max    214717.057100  212147.622600


# Create a scatter matrix to visualize relationships between numeric columns
fig_scatter = plotly_x.scatter(df_top_stations, x='arrivals', y='departures', color='station_name',
                         title='Morning Arrivals vs Evening Departures')
fig_scatter.show()

• df_top_stations.describe() provides summary statistics for the numerical columns
• plotly_x.scatter() creates scatter plots to visualize relationships between numerical columns

These statistics can help us identify trends, correlations, and relationships in our data, allowing us to gain insights and make informed decisions about further analysis or modeling.

👍 Data correlation refers to the degree to which two or more variables change together. It indicates the strength and direction of the linear relationship between variables. The correlation coefficient is a value between -1 and 1. A 1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation. A correlation coefficient near 0 suggests a weak or no linear relationship between the variables.

Hypothesis Testing

In hypothesis testing, we use statistical methods to validate assumptions and draw conclusions. On the previous scatter chart, we can see that there appears to not be a strong correlation between arrivals and departures for the top 10 stations with most arrivals. This fact could be an area of interest for the analysis, and we may want to take a deeper look by running a test.

# Perform Pearson correlation test
def test_arrival_departure_correlation(df: pd.DataFrame, label: str) -> None:
   corr_coefficient, p_value = pearsonr(df['arrivals'], df['departures'])   
   p_value = round(p_value, 5)

   if p_value < 0.05:
      conclusion = f"The correlation {label} is statistically significant."
   else:
      conclusion = f"The correlation {label} is not statistically significant."

   print(f"Pearson Correlation {label} - Coefficient : {corr_coefficient} P-Value : {p_value}")    
   print(f"Conclusion: {conclusion}")

test_arrival_departure_correlation(df_top_stations, 'top-10 stations')
test_arrival_departure_correlation(df_correlation, 'all stations')

# output
Pearson Correlation top-10 stations - Coefficient : -0.14112 P-Value : 0.69738
Conclusion: The correlation top-10 stations is not statistically significant.
Pearson Correlation all stations - Coefficient : 0.73803 P-Value : 0.0
Conclusion: The correlation all stations is statistically significant.

Let's take a look at the output and explain what is going on. A correlation coefficient of -0.14 suggests a weak negative correlation between the variables being analyzed. The p-value of 0.69 is relatively high, which suggests that there is no real correlation between morning arrivals and night departures for the top stations. This means that the high number of arrivals in that morning is not reflecting a correlation of departures in the evening.

If we compare the entire data frame with all the stations, we can see a correlation .73 (close to 1) and a p-value of 0 which indicates that there is a statistically significant correlation for the entire dataset, which means that other stations had an increase in departures compared to its arrivals. By looking at the entire data sample, we can see there is in fact a correlation, and the increase in arrivals directly impacts departures later in the day.

Business Intelligence and Reporting

Business intelligence (BI) is a strategic approach that involves the collection, analysis, and presentation of data to facilitate informed decision-making within an organization. In the context of business analytics, BI is a powerful tool for extracting meaningful insights from data and turning them into actionable strategies.

A Business Analyst (BA) uses a systematic approach to uncover valuable insights from data. As example, by calculating the total number of passengers for arrivals and departures, we gain a comprehensive understanding of passenger flow dynamics. Furthermore, we can employ distribution analysis to investigate variations across stations, days of the week, and time slots. These analyses provide essential insights for business strategy and decision-making, allowing us to identify peak travel periods, station preferences, and time-specific trends that directly influence business operations.

# Calculate total passengers for arrivals and departures
total_arrivals = df['exits'].sum() 
total_departures = df['entries'].sum() 

print(f"Total Arrivals: {total_arrivals} Total Departures: {total_departures}")

# output
Total Arrivals: 2954513147693 Total Departures: 3691269727684

# Create distribution analysis by station
df_by_station = df.groupby(["station_name"], as_index=False)[measures].sum()
print(df_by_station.head(5))

#output
 station_name      entries        exits
0         1 AV  41921835330   4723874242
1       103 ST   1701063755   1505114656
2       104 ST  60735889120  35317207533
3       111 ST   1856383672    840818137
4       116 ST   7419106031   8292936323

# Create distribution analysis by day of the week
df_by_date = df.groupby(["created_dt"], as_index=False)[measures].sum()
day_order = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']
df_by_date["weekday"] = pd.Categorical(df_by_date["created_dt"].dt.strftime('%a'), categories=day_order, ordered=True)
df_entries_by_date =  df_by_date.groupby(["weekday"], as_index=False)[measures].sum()
print(df_entries_by_date.head(5))

# output
weekday       entries         exits
0     Sun   83869272617   53997290047
1     Mon  839105447014  667971771875
2     Tue  723988041023  592238758942
3     Wed  728728461351  594670413050
4     Thu   80966812864   51232966458

# Create distribution analysis time slots
for slot, (start_hour, end_hour) in time_slots.items():
    slot_data = df[(df['created_dt'].dt.hour >= start_hour) & (df['created_dt'].dt.hour <= end_hour)]
    arrivals = slot_data['exits'].sum()
    departures = slot_data['entries'].sum()
    print(f"{slot.capitalize()} - Arrivals: {arrivals:.2f}, Departures: {departures:.2f}")
# output
Morning - Arrivals: 494601773970.00, Departures: 619832037915.00
Afternoon - Arrivals: 493029769709.00, Departures: 615375337214.00
Night - Arrivals: 814729184132.00, Departures: 1008230417627.00

BI analysis is important in helping us understand the data, which can then be communicated to stakeholders so that we can make decisions based on which information is more relevant to the organization.

Data Visualization

Data visualization is a powerful tool that takes the insights derived from data analysis and presents them in a visual format. While tables with numbers on a report provide raw information, visualizations allow us to grasp complex relationships and trends at a glance. Dashboards, in particular, bring together various visual components like charts, graphs, and scorecards into a unified interface.

Imagine a scenario where we have analyzed passenger data using Python and determined that certain stations experience higher passenger volumes during specific times of the day. Translating this into a dashboard, we can use donut graphs to show the distribution of passenger counts on stations, bar graphs to visualize passenger trends over different times of the day, and scorecards to highlight key metrics like total passengers.

Such a dashboard offers a comprehensive view of the data, enabling quick comparisons, trend identification, and actionable insights. Instead of sifting through numbers, stakeholders can directly observe the patterns, correlations, and anomalies, leading to informed decision-making. This visualization approach enhances communication, collaboration, and comprehension among teams, making it an essential tool for data-driven organizations.

Types of Data Visualizations

There are a few terms that are used interchangeably when it comes to data visualization, but in reality there are subtle differences and specific uses between the terms. Let's review them in more details.

• Chart: A chart is a visual representation that displays data points, trends, and patterns. It uses graphical elements such as bars, lines, or pie charts to depict data relationships. Charts are focused on illustrating specific data comparisons or distributions, making it easier for viewers to understand data at a glance.

• Graph: A graph is a broader term that encompasses both charts and diagrams. It's used to represent data visually and can include a variety of visual elements, including nodes, edges, bars, lines, and more. Graphs are often used to showcase relationships and connections among various data points, allowing viewers to understand complex structures or networks

• Report: A report is a structured document that provides a comprehensive overview of data analysis, findings, and insights. It typically includes a mix of textual explanations, tables, charts, and graphs. Reports are designed to convey detailed information and can be several pages long. They often include an executive summary, methodology, results, and recommendations

• Dashboard: A dashboard is a visual display of key performance indicators (KPIs) and metrics that offers a real-time snapshot of business data. Dashboards consolidate multiple visual elements like charts, graphs, gauges, and tables onto a single screen. They are interactive and customizable, allowing users to monitor trends, and identify anomalies. Dashboards provide a quick and holistic view of business performance

In summary, a chart is a specific type of visual representation focusing on data points, a graph represents broader data relationships, a report is a structured document presenting detailed analysis, and a dashboard is an interactive screen displaying real-time KPIs and metrics. Each serves a unique purpose in effectively communicating information to different types of audiences.

Dashboard Design Principles

Designing effective dashboards requires attention to several key principles to ensure clarity, usability, and the ability to convey insights. Here's a short list of essential Dashboard Design Principles:

• User-Centered Design: Understand our audience and their needs. Design the dashboard to provide relevant and actionable information to specific user roles, executives only want to see the big picture not details

• Clarity and Simplicity: Keep the design clean and uncluttered. Use a simple layout, meaningful titles, and avoid unnecessary decorations

• Consistency: Maintain a consistent design across all dashboard components. Use the same color schemes, fonts, and visual styles to create a cohesive experience

• Master Filter: Include a master filter that allows users to select a date range, segment, or other criteria. This synchronizes data across all components, ensuring a unified view

• Data Context and Relationships: Clearly label components and provide context to explain data relationships. Help users understand the significance of each element

• Whitespace: Use whitespace effectively to separate components and enhance readability. Proper spacing reduces visual clutter

• Real-Time Updates: If applicable, ensure that the dashboard provides real-time or near-real-time data updates for accurate decision-making

• Mobile Responsiveness: Design the dashboard to be responsive across various devices and screen sizes, ensuring usability on both desktop and mobile

• Testing and Iteration: Test the dashboard with actual users and gather feedback. Iterate on the design based on user insights and preferences

Effective dashboard design not only delivers data but also tells a story. It guides users through insights, highlights trends, and supports data-driven decision-making. Applying these principles will help create dashboards that are intuitive, informative, and impactful.

Data Visualization Tools

The data visualization tools can be divided into code-centric and low-code solutions. A code-centric solution involves writing programs to manage the data analysis and visuals. A low-code solution uses cloud-hosted tools that accelerate the data analysis and visualization. Instead of focusing on code, a low-code tool enables data professionals to focus on the data. Let's review some of these tools in more detail:

• Python, coupled with libraries like Plotly, offers a versatile platform for data visualization that comes with its own set of advantages and limitations. This code-centric approach enables data professionals to integrate data analysis and visualization seamlessly, and they are particularly suited for individual research, in-depth analysis, and presentations in a controlled setting

• Looker Studio is a powerful low-code, cloud-hosted business intelligence and data visualization platform that empowers organizations to explore, analyze, and share insights from their data. It offers a user-friendly interface that allows users to create interactive reports, dashboards, and visualizations

• Microsoft Power BI is a widely-used low-code, cloud-hosted data visualization and business intelligence tool. It seamlessly integrates with other Microsoft tools and services, making it a popular choice for organizations already in the Microsoft ecosystem. Power BI offers an intuitive drag-and-drop interface for building interactive reports and dashboards. Its extensive library of visuals and custom visuals allows users to create compelling data representations
• Tableau, acquired by Salesforce, is renowned for its cloud-hosted and low-code data visualization capabilities. It provides users with an array of options for creating dynamic and interactive visuals. Tableau's "drag-and-drop" approach simplifies the process of connecting to various data sources and creating insightful dashboards.

Each of these tools offers unique features and benefits, catering to different user preferences and organizational needs. Whether it's Looker's focus on data modeling, Power BI's integration with Microsoft products, or Tableau's flexibility and advanced analytics capabilities, these tools play a significant role in empowering users to unlock insights from their data.

Summary

Data analysis involves meticulous exploration, transformation, and comprehension of raw data to identify meaningful insights. There are guidelines and design patterns to follow for each specific use case. A BA might focus on KPIs, while a QAE might focus on statistical analysis of process quality. These insights, however, find their true value through data visualization. A code-centric approach with Python, aided by Plotly, offers potent tools for crafting analyses and visuals, but a low-code cloud hosted solution is often the solution for broader sharing and enterprise solutions.

In conclusion, the synergy between data analysis and visualization is pivotal for data-driven projects. Navigating data analysis with established principles and communicating insights through visually engaging dashboards empowers us to extract value from data. Whether opting for code-centric or low-code solutions, the choice of tooling and platform hinges on the balance between team expertise and target audience.

Exercise - Data Analysis and Visualization

With a better understanding of the data analysis and visualization process, the next step is to put these concepts into practice through a hands-on exercise. In this lab, we can continue our data engineering process and create a dashboard that will meet the requirements established in the discovery phase.

👉 Data Engineering Process Fundamentals - Data Analysis and Visualization Exercise

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👍 Originally published by ozkary.com