article thumbnail

Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

Snapshots – These implements type-2 slowly changing dimensions (SCDs) over mutable source tables. Seeds – These are CSV files in your dbt project (typically in your seeds directory), which dbt can load into your data warehouse using the dbt seed command. An Amazon Simple Storage (Amazon S3) bucket to host documentation files.

article thumbnail

Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. It will never remove files that are still required by a non-expired snapshot.

Snapshot 103
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Patterns for updating Amazon OpenSearch Service index settings and mappings

AWS Big Data

One the major advantages of using the _reindex operation is that it doesn’t require placing the source index in a read-only mode (data producers may continue to write the data while reindexing is in progress). With the _reindex operation, you can copy all or a subset of documents that you select through a query to another index.

article thumbnail

A Summary Of Gartner’s Recent Innovation Insight Into Data Observability

DataKitchen

Like an apartment blueprint, Data lineage provides a written document that is only marginally useful during a crisis. This is especially true regarding our one-to-many, producer-to-consumer relationships on our data architecture. It’s primarily used to understand where data came from and its transformations.

article thumbnail

Exploring real-time streaming for generative AI Applications

AWS Big Data

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. For example, Amazon DynamoDB provides a feature for streaming CDC data to Amazon DynamoDB Streams or Kinesis Data Streams.

article thumbnail

“You Complete Me,” said Data Lineage to DataOps Observability.

DataKitchen

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130
article thumbnail

Choosing an open table format for your transactional data lake on AWS

AWS Big Data

Amazon Redshift only supports Delta Symlink tables (see Creating external tables for data managed in Delta Lake for more information). Refer to Working with other AWS services in the Lake Formation documentation for an overview of table format support when using Lake Formation with other AWS services.

Data Lake 115