Unlocking Parquet Data Analytics: Amazon SageMaker Canvas, Athena, and AWS Lake Formation Unite

Unlocking Parquet Data Analytics: Amazon SageMaker Canvas, Athena, and AWS Lake Formation Unite

Unlocking Parquet Data Analytics: Amazon SageMaker Canvas, Athena, and AWS Lake Formation Unite

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In today’s era of data-driven decision making, the importance of data in machine learning algorithms cannot be overstated. When it comes to storing large quantities of data, Apache Parquet format comes to the forefront as a popular choice for its efficient columnar storage. In this scenario, Amazon SageMaker Canvas emerges as a game-changing tool that simplifies access to Parquet files and enables data import from more than 40 sources, including Amazon Athena. The purpose of this article is to explain how to efficiently query Parquet files with Athena using AWS Lake Formation and use the output in Canvas to train a model.

Key Points

  1. Solution Overview:
    Amazon Athena serves as a serverless, interactive analytics service that operates on open-source frameworks, including Apache Parquet. Athena offers compatibility with several data formats, such as CSV, TSV, JSON, text files, open-source columnar formats like ORC and Parquet, and even compressed data in formats like Snappy, Zlib, LZO, and GZIP. On the other hand, AWS Lake Formation functions as an integrated service offering ingestion, cleaning, cataloging, transformation, and securing of data for analysis and ML purposes.

  2. Set up the Lake Formation:
    To enable analysts using Canvas to access Parquet data, a one-time setup of the Lake Formation database is necessary. Setting up the Lake Formation entails creating an environment that simplifies data management and ensures efficient querying of Parquet data by Canvas.

  3. Grant Lake Formation Access Permissions to Canvas:
    To ensure smooth use of registered data in Amazon S3 by services such as AWS Glue, Athena, Amazon Redshift, Amazon QuickSight, and Amazon EMR with Zeppelin notebooks and Apache Spark, it is crucial to grant access permissions to Canvas. This process ensures that users can seamlessly access and manage their data across multiple AWS services.

  4. Import the Parquet Data to Canvas using Athena:
    Once the required permissions are granted, proceed with importing Parquet data to Canvas for building ML models. For illustration, consider a consumer electronics business with historical time series data. This data can be leveraged to create ML models that predict future trends, enabling the business to optimize its operations. Importing Parquet data allows analysts to have all the necessary information at their fingertips, facilitating the model creation process.

  5. Build ML Models with Canvas using Imported Parquet Data:
    With the Parquet data imported into Canvas, users can now build and train machine learning models. The straightforward interface that Canvas provides makes it an ideal solution for businesses to create their ML models, expediting the analysis process and accelerating data-driven decision-making.

Leveraging Athena to query Parquet files with AWS Lake Formation and using the resulting data to build models in Amazon SageMaker Canvas streamlines data access and management while enhancing the ML model-building experience. By following the steps outlined in this article, organizations can efficiently unlock the potential of their Parquet data and extract invaluable insights to drive business growth and innovation.

Casey Jones Avatar
Casey Jones
12 months ago

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