Introduction

A data lake is a central storage system that stores data in its raw format. It is used to collect huge amounts of data that are yet to be analyzed by analysts and data scientists or for regulatory purposes. As the amount of information and the variety of data that a company operates with increases, it gets increasingly difficult to preprocess and store it in a traditional data warehouse. By design, data lakes are built to handle unstructured and semi-structured data with no pre-defined schema.On-premise data lakes are difficult to scale and require thorough requirements and cost estimations. Cloud data lakes are often considered an easier-to-use and -scale alternative. In this chapter, we will go through a set of recipes that will help you to launch a data lake, load data from external storage, and build ETL/ELT pipelines around it.Azure Data Lake Gen2 can store both structured and unstructured data. In this chapter, we will load and manage our datasets in Azure Data Lake...

Technical requirements

You need to have access to Microsoft Azure with Synapse workspace resource created. An Azure free account is sufficient for all recipes in this chapter. To create an account, use the following link: https://azure.microsoft.com/free/. To create Synapse workspace resource please refer to Chapter 3.

Setting up Azure Data Lake Storage Gen2

Azure Data Lake Storage Gen2 is a versatile solution that can be used as a single storage platform.It is Hadoop compatible, so you can use it with HDInsights and Databricks, which we will cover in the next chapter.Setting up properly configured storage is a critical operation for developers and data engineers. In this section, we will set up and configure a scalable Azure data lake to be used with Azure Data Factory and Azure Synapse Analytics.

Getting ready

To get started with your recipe, log in to your Microsoft Azure account.

How to do it...

Azure Data Lake Gen2 uses hierarchical namespaces. Unless you already have a storage account with hierarchical namespaces, you will have to create a new one.Now that we have set up the resource group, let's create a storage account:

1. Search for Storage accounts in the Azure search bar and click on it.
2. To add a new storage account, click + Add.
3. Select Azure Subscription and Resource Group.

4. Add a Storage...

Create Synapse Analytics Spark Pool

A Spark Pool is a fundamental component that provides the computing resources for running large-scale Apache Spark jobs within Synapse Analytics.In this receipt, we'll guide you through the process of creating a Spark Pool in Azure Synapse. You'll learn how to configure pool settings, customize resource allocation, manage credentials, and monitor job progress.By the end, you'll have the knowledge to provision and configure a Spark Pool in Azure Synapse, enabling you to harness the power of Apache Spark for high-performance data processing and analytics.

Getting ready

To get started with your recipe, log in to your Microsoft Azure account. You need to have Synapse workspace created.

How to do it...

To create a Synapse Analytics Spark pool, follow these steps:

1. Log in to the Azure portal and navigate to your Synapse Analytics workspace.
2. Click on the "Manage" button in the left-hand menu and select "Apache Spark pools"...

Integrate Azure Data Lake and run Spark Pool jobs

In this receipt, we'll explore how to integrate Azure Data Lake with Spark Pool in Azure Synapse Analytics. By combining these services, we can unlock powerful data processing and analysis workflows. We'll cover the steps to establish the connection, run Spark jobs, and leverage the capabilities of both services. Get ready to harness the potential of Azure Data Lake and Spark Pool for efficient and scalable data processing.

Getting ready

Let's load and preprocess the MovieLens dataset (F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872). It contains ratings and free-text tagging activity from a movie recommendation service.The MovieLens dataset exists in a few sizes, which have the same structure. The...

Build and Orchestrate data pipeline for Data Lake and Spark

In this receipt, we will explore the process of constructing and orchestrating data pipelines using Data Lake and Spark in Azure Synapse Analytics. By leveraging the capabilities of these services, you can efficiently manage and process large amounts of data for analysis and insights.

1. We will improve the code in the notebook from the previous receipt to create a data transformation job and orchestrate it from Synapse Integrate. You'll learn how to extract, transform, and load data into a data lake using Spark, building efficient and scalable pipelines.
2. Additionally, we'll cover the crucial aspect of orchestration, where you'll discover how to schedule, monitor, and manage the execution of your data pipelines using Azure Synapse's orchestration capabilities.

Getting ready

To get started with your recipe, log in to your Synapse Analytics workspace. You’ll need to have the notebook created in the previous...

Ingesting data into Delta Lake using Mapping Data Flows

In the realm of data management, Atomicity, Consistency, Isolation, Durability (ACID) is a foundational set of principles ensuring the reliability and integrity of database transactions. Let’s break down the significance of each component:

• Atomicity: Guarantees that a transaction is treated as a single, indivisible unit. It either executes in its entirety, or not at all. This ensures that even if a system failure occurs mid-transaction, the database remains in a consistent state.
• Consistency: Enforces that a transaction brings the database from one valid state to another. Inconsistent states are avoided, providing a reliable and predictable environment for data operations.
• Isolation: Ensures that transactions operate independently of each other, preventing interference. Isolation safeguards against concurrent transactions affecting each other’s outcomes, maintaining data integrity.
• Durability...

External integrations with other compute engines (Snowflake)

Azure Data Factory (ADF), a powerful cloud-based data integration service from Microsoft, has emerged as the go-to solution for enterprises seeking efficient and scalable data movement across various platforms. With its extensive capabilities, ADF not only enables seamless data integration within the Azure ecosystem but also offers external integrations with leading compute engines such as Snowflake.

Azure Data Factory’s integration with Snowflake enables enterprises to seamlessly leverage cloud data warehousing capabilities. Snowflake’s architecture, built for the cloud, complements Azure’s cloud-native approach, offering a scalable and elastic solution for storing and processing vast amounts of data. The integration supports the creation of cost-effective and scalable data solutions. Azure Data Factory’s ability to dynamically manage resources and Snowflake’s virtual warehouse architecture...