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All-SQL Pipelines for Building Data as a Product on AWS
SQLake makes it easy to build data pipelines that ingest data at rest and in motion from a wide variety of sources. Data engineers, analysts and data scientists write SQL to prepare, transform and model the data before publishing the results to the data warehouse and data lake. Read on to learn how SQLake:
- Generates an optimized execution and orchestration plan that ensures data is delivered in a reliable, consistent and performant manner.
- Automatically detects, infers and updates schema and table metadata, optimizes files for best query performance and manages inserts, updates and deletes in the data lake and data warehouse.
- Seamlessly integrates with AWS services such as AWS Glue Data Catalog, Amazon S3, Amazon Kinesis, Amazon Athena, Amazon Redshift, and Amazon SageMaker.
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Write a query, get a pipeline: Transform batch and streaming data and automate time-consuming orchestration and optimization with Upsolver SQLake. Delivering data as a product must begin with your data pipeline. With Upsolver SQLake on AWS, organizations quickly and easily deliver data as a product, without needing to manage infrastructure, orchestration or data engineering best practices.
This guide is for:
- Data engineering leaders who want to understand how to solve practical security and privacy challenges on cloud data lakes
- CIOs who want to ensure their organization is compliant and reduce operational risks
- CTOs who want to discover cloud architecture best practices
