Remove Data Lake Remove Data Processing Remove Metadata
article thumbnail

Modernize your legacy databases with AWS data lakes, Part 2: Build a data lake using AWS DMS data on Apache Iceberg

AWS Big Data

This is part two of a three-part series where we show how to build a data lake on AWS using a modern data architecture. This post shows how to load data from a legacy database (SQL Server) into a transactional data lake ( Apache Iceberg ) using AWS Glue. To start the job, choose Run. format(dbname)).config("spark.sql.catalog.glue_catalog.catalog-impl",

article thumbnail

Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

Data Lake 110
Insiders

Sign Up for our Newsletter

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

article thumbnail

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. An entity can act both as a producer of data assets and as a consumer of data assets.

article thumbnail

Enrich your serverless data lake with Amazon Bedrock

AWS Big Data

For many organizations, this centralized data store follows a data lake architecture. Although data lakes provide a centralized repository, making sense of this data and extracting valuable insights can be challenging.

article thumbnail

Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 110
article thumbnail

Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

article thumbnail

How Cargotec uses metadata replication to enable cross-account data sharing

AWS Big Data

Cargotec captures terabytes of IoT telemetry data from their machinery operated by numerous customers across the globe. This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization. The target accounts read data from the source account S3 buckets.