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Best Practices for Building Your Azure Data Lake Storage Gen2

Learn about Azure Data Lake Storage Gen2 — when to use it and how to optimize and ease your implementation.

Vish Margasahayam StreamSets
By and Posted in Advanced Analytics October 5, 2023

Azure Data Lake Storage Gen2 (ADLS Gen2) has emerged as a powerful platform for storing, managing, and processing vast amounts of data. In this blog post, we’ll explore the core functionalities of ADLS Gen2, its key differentiators from other data lake solutions, and its distinct advantages over data warehouses.

What Is Azure Data Lake Storage Gen2 Used For?

ADLS Gen2 is a comprehensive data storage and analytics platform that handles large volumes of structured and unstructured data. It is best suited for big data and advanced analytics use cases, empowering organizations to gain deeper insights from diverse data sources. Unlike traditional data warehouses, ADLS Gen2 provides a scalable and cost-effective solution for handling massive datasets with varying structures.

Azure Data Lake Storage Gen2 Implementation Best Practices

Implementing ADLS Gen2 requires a strategic approach to harness its full potential. Let’s explore the best practices that will guide you in building a robust and efficient data lake.

Optimize for Data Ingest:

When ingesting data from source systems, carefully select appropriate hardware and network connectivity. Use Solid State Drives (SSD) for disk hardware and high-speed Network Interface Controllers (NIC) for network hardware. Consider using dedicated links with Azure ExpressRoute for on-premises source data. For cloud-based source data, ensure the data is in the same Azure region as your Data Lake storage-enabled account to achieve optimal performance.

Structure Data Sets:

Pre-planning the structure of your data is crucial for optimizing performance and cost. Choose appropriate file formats (e.g., Avro, Parquet, ORC) based on your I/O patterns and query requirements. Larger file sizes (256 MB to 100 GB) generally lead to better performance and reduced costs. Organize your data into a directory structure that suits your workload, such as IoT, batch jobs, or time-series data. Data integration tools like StreamSets can help organize and structure data during pre-planning, migration efforts, and regular ingestion phases.  

Set Up Security:

Security is paramount when working with data in ADLS Gen2. Ensure that data movement and access are appropriately safeguarded by following these best practices. Enforce your hierarchical file system’s security permissions on directories and files using Azure role-based access control (Azure RBAC) roles and access control lists (ACLs). Only assign appropriate permissions, limiting access to just the necessary data and operations.

Monitor Telemetry:

Implement effective monitoring to track usage and performance. Utilize Azure Storage logs in Azure Monitor to monitor frequent operations, high-latency operations, and service-side throttling. Decide where to store logs based on your access requirements, such as near real-time query or long-term retention, and configure diagnostic settings accordingly.

Ease Your Data Lake Implementation With StreamSets

StreamSets simplifies and enhances your ADLS Gen2 implementation. Its powerful data integration platform empowers you to follow best practices effortlessly, from automation to diverse file formats, ensuring a successful and optimized data lake setup.

For example, with StreamSets, you can adjust the batch and buffer sizes appropriately based on your data volume and network conditions. Larger batch sizes can improve performance but may require more memory. You can experiment with different configurations to find the right balance for your business. 

StreamSets also supports parallelism. You can utilize parallelism settings in StreamSets to distribute data ingestion across multiple threads, maximizing the use of available resources. Using the StreamSets platform, you can carefully assess your infrastructure’s capabilities and adjust the parallelism settings accordingly.

Finally, within StreamSets, you are able to enable data compression with an Avro-supported codec during ingestion to reduce the amount of data sent over the network. Compressing data can significantly enhance transfer speed and reduce storage costs in ADLS Gen2.

Visit the documentation for more information on what a successful ADLS Gen2 implementation can look like.

In conclusion, adopting best practices and leveraging StreamSets will set you on the path to building a highly efficient and future-ready Azure Data Lake Storage Gen2, unlocking the full potential of your data-driven insights.

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