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prasanth595/aws-data-analytics

v1.5.0Apache-2.0

Data lake, analytics, search, streaming, ETL, and orchestration workflows with S3 Tables, AWS Glue, Athena, Amazon Redshift, Amazon OpenSearch Service, Amazon MSK, Amazon Managed Service for Apache Flink, Amazon MWAA, and Amazon SageMaker Catalog. Covers managed Iceberg tables on S3 Tables, ingestion from JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Amazon Redshift, Snowflake, BigQuery, and DynamoDB, AWS Glue Data Catalog inventory and asset discovery, federated Athena queries, vector storage and semantic search on Amazon S3 Vectors, Amazon OpenSearch Service and Serverless (migration from Solr/Elasticsearch, provisioning, vector/semantic/hybrid search, log analytics, trace analytics), Amazon MSK Provisioned with Standard and Express brokers (cluster sizing, performance and consumer lag troubleshooting, topic management, MSK Replicator for disaster recovery and active-active replication, and Streaming Tables delivery to S3 Tables), migrating self-managed Apache Kafka to MSK Express, Apache Flink application development on Amazon Managed Service for Apache Flink, and Amazon MWAA Provisioned and MWAA Serverless (authoring, testing, and debugging DAGs and workflows, and Airflow version upgrades for provisioned environments), and Amazon SageMaker Catalog and AWS Glue Data Catalog curation (business descriptions, glossaries, metadata forms, publishing, and safe asset retirement) with subscription-based access requests, approvals, audits, and revocations.

ingesting-into-data-lake

Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.

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