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Introducing Amazon MWAA larger environment sizes

AWS Big Data

Running Apache Airflow at scale puts proportionally greater load on the Airflow metadata database, sometimes leading to CPU and memory issues on the underlying Amazon Relational Database Service (Amazon RDS) cluster. A resource-starved metadata database may lead to dropped connections from your workers, failing tasks prematurely.

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6 DataOps Best Practices to Increase Your Data Analytics Output AND Your Data Quality

Octopai

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. SPC is the continuous testing of the results of automated manufacturing processes.

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Addressing Data Mesh Technical Challenges with DataOps

DataKitchen

The domain also includes code that acts upon the data, including tools, pipelines, and other artifacts that drive analytics execution. The domain requires a team that creates/updates/runs the domain, and we can’t forget metadata: catalogs, lineage, test results, processing history, etc., ….

Testing 246
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Run Trino queries 2.7 times faster with Amazon EMR 6.15.0

AWS Big Data

Benchmark setup In our testing, we used the 3 TB dataset stored in Amazon S3 in compressed Parquet format and metadata for databases and tables is stored in the AWS Glue Data Catalog. This benchmark uses unmodified TPC-DS data schema and table relationships. With Amazon EMR 6.10.0 If you are using Amazon EMR 6.8.0

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Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore federation

AWS Big Data

Organizations have multiple Hive data warehouses across EMR clusters, where the metadata gets generated. To address this challenge, organizations can deploy a data mesh using AWS Lake Formation that connects the multiple EMR clusters. Test access using Athena queries in the consumer account.

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DataOps Facilitates Remote Work

DataKitchen

Data Science Workflow – Kubeflow, Python, R. Data Engineering Workflow – Airflow, ETL. Data Visualization, Preparation – Self-service tools sucha as Tableau, Alteryx. Data Governance/Catalog (Metadata management) Workflow – Alation, Collibra, Wikis.

Testing 147
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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. 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.

Data Lake 118