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Load data incrementally from transactional data lakes to data warehouses

AWS Big Data

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure.

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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Data architecture has evolved significantly to handle growing data volumes and diverse workloads. Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data.

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Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality

AWS Big Data

Today, many customers build data quality validation pipelines using its Data Quality Definition Language (DQDL) because with static rules, dynamic rules , and anomaly detection capability , its fairly straightforward. One of its key features is the ability to manage data using branches.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. and later supports the Apache Iceberg framework for data lakes. The snapshot points to the manifest list. AWS Glue 3.0

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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Solving the small file problem and improving query performance In modern data architectures, stream processing engines such as Amazon EMR are often used to ingest continuous streams of data into data lakes using Apache Iceberg. SparkActions.get().expireSnapshots(iceTable).expireOlderThan(TimeUnit.DAYS.toMillis(7)).execute()

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Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt

AWS Big Data

Complex queries, on the other hand, refer to large-scale data processing and in-depth analysis based on petabyte-level data warehouses in massive data scenarios. In this post, we use dbt for data modeling on both Amazon Athena and Amazon Redshift. Here, data modeling uses dbt on Amazon Redshift.