DP-203 Data Engineering on Microsoft Azure

Process time series data

Concepts

Processing time series data is a crucial task in the field of data engineering. Time series data, which consists of a sequence of data points indexed in chronological order, can provide valuable insights and enable predictions for various applications. In this article, we will explore how to process time series data using Microsoft Azure.

Azure Time Series Insights

Azure Time Series Insights is a fully managed analytics, visualization, and storage service that simplifies the analysis of time series data. It allows you to explore and monitor time series data in near real-time with interactive charts and graphs. With Time Series Insights, you can easily identify trends, anomalies, and patterns in your data.

To get started with Time Series Insights, you need to create an environment and configure an event source. The event source could be an IoT hub, an Event Hubs namespace, or a custom solution using the Time Series Insights API. Once the event source is configured, you can ingest data into Time Series Insights for processing and analysis.

Let’s take a look at an example of how to ingest and process time series data using Azure Time Series Insights. First, we need to create an environment in the Azure portal. Once the environment is created, we can configure the event source by providing the necessary details like the event source type, connection string, and mapping properties.

// Creating an environment in Azure Time Series Insights CREATE ENVIRONMENT WITH NAME '{EnvironmentName}' RESOURCE GROUP '{ResourceGroup}' LOCATION '{Location}' SKU NAME 'S1';


// Configuring an event source

CREATE EVENT SOURCE

    WITH NAME '{EventSourceName}'

    ENVIRONMENT '{EnvironmentName}'

    EVENT SOURCE TYPE '{EventSourceType}'

    CONNECTION STRING '{ConnectionString}'

    KEY NAME '{KeyName}'

    KEY VALUE '{KeyValue}'

    MAPPING PROPERTIES '{

        "Tag1": "$.property1",

        "Tag2": "$.property2"

    }';

Once the event source is configured, you can start ingesting data into Time Series Insights using various methods like REST APIs, Azure Functions, or Azure Stream Analytics.

Azure Stream Analytics

Azure Stream Analytics is a real-time analytics service that allows you to process and analyze streaming data from various sources, including time series data. Stream Analytics provides a SQL-like language for defining queries and transformations on the incoming data.

To process time series data using Azure Stream Analytics, you need to set up an input source, define a query, and configure an output sink. The input source could be an Event Hub, IoT Hub, or Blob storage. The query defines how the incoming data is transformed and filtered, and the output sink determines where the processed data is stored or sent.

Here’s an example of how to process time series data using Azure Stream Analytics:

-- Setting up an input source CREATE INPUT WITH NAME '{InputName}' PROVIDER '{Provider}' CONNECTION STRING '{ConnectionString}' FORMAT 'JSON';


-- Defining a query

CREATE FUNCTION

    WITH NAME '{FunctionName}'

    RETURNS {ReturnType}

    AS '{JavaScript Code}';

SELECT *

INTO {OutputSink}

FROM {InputSource}

WHERE {Condition}

GROUP BY {Grouping};
-- Configuring an output sink

CREATE OUTPUT

    WITH NAME '{OutputName}'

    PROVIDER '{Provider}'

    CONNECTION STRING '{ConnectionString}'

    FORMAT 'JSON';
-- Binding the input source, query, and output sink

BINDING

    SOURCE = "{InputName}",

    TRANSFORMATION = "{FunctionName}",

    SINK = "{OutputName}";

Azure Databricks

Azure Databricks is an Apache Spark-based analytics platform that provides a collaborative environment for processing big data and performing advanced analytics. Databricks enables you to efficiently process and analyze time series data using distributed computing capabilities.

To process time series data using Azure Databricks, you can leverage the power of Spark’s DataFrame API and libraries like PySpark or Scala. You can load time series data from various sources, perform transformations and aggregations, apply machine learning algorithms, and visualize the results.

Here’s an example of how to process time series data using PySpark in Azure Databricks:

# Loading time series data from a CSV file df = spark.read.format('csv').options(header='true').load('{FilePath}');


# Converting the timestamp column to a datetime type

df = df.withColumn('{TimestampColumn}', df['{TimestampColumn}'].cast('timestamp'));
# Aggregating data by a specific time interval

df = df.groupBy(window('{TimestampColumn}', '{TimeInterval}')).agg({Aggregation});

# Performing further transformations or analyses

# ...

# Saving the processed data to a file or database df.write.format('{OutputFormat}').options('{Options}').save('{OutputPath}');

In this article, we explored the process of time series data using Microsoft Azure services. We discussed Azure Time Series Insights for interactive analysis, Azure Stream Analytics for real-time processing, and Azure Databricks for advanced analytics. By leveraging these services and tools, you can efficiently handle and gain insights from your time series data in the Azure ecosystem.

Answer the Questions in Comment Section

Which Azure service is used to process time series data in real-time?
- a) Azure Data Lake Analytics
- b) Azure Stream Analytics
- c) Azure Databricks
- d) Azure Machine Learning
Correct answer: b) Azure Stream Analytics

True/False: Azure Data Factory supports processing time series data.

Correct answer: True
Which Azure service can be used for storing and analyzing large volumes of time series data?
- a) Azure Analysis Services
- b) Azure Cosmos DB
- c) Azure Synapse Analytics
- d) Azure Data Explorer
Correct answer: d) Azure Data Explorer
How can you handle late-arriving events in Azure Stream Analytics?
- a) Ignore the late events
- b) Store late events in a separate output path
- c) Retry processing the late events
- d) Drop the late events and trigger an alert
Correct answer: b) Store late events in a separate output path
When using Azure Stream Analytics, which query language is used for processing time series data?
- a) SQL
- b) Python
- c) C#
- d) Scala
Correct answer: a) SQL
True/False: Azure Time Series Insights is a managed analytics service for analyzing time series data.

Correct answer: True
Which Azure service can be used for predictive analytics on time series data?
- a) Azure Data Factory
- b) Azure Machine Learning
- c) Azure Stream Analytics
- d) Azure Databricks
Correct answer: b) Azure Machine Learning
What is the benefit of using Azure Data Explorer for time series data analysis?
- a) Built-in support for geospatial data
- b) Real-time data ingestion and indexing
- c) Seamless integration with Azure Machine Learning
- d) Automatic anomaly detection capabilities
Correct answer: b) Real-time data ingestion and indexing
True/False: Azure Databricks provides built-in functions for processing time series data.

Correct answer: True

Which Azure service can be used to build custom machine learning models for time series forecasting?
- a) Azure Data Factory
- b) Azure Machine Learning
- c) Azure Stream Analytics
- d) Azure Databricks
Correct answer: d) Azure Databricks