AWS Big Data Blog

Building a modern data platform on AWS enables you to collect data of all types, store it in a central, secure repository, and analyze it with purpose-built tools. Yet you may be unsure of how to get started and the impact of certain design decisions. To address the need to provide advice tailored to specific technology and application domains, AWS added the concept of well-architected lenses 2017. AWS now is happy to announce the Analytics Lens for the AWS Well-Architected Framework. This post provides an introduction of its purpose, topics covered, common scenarios, and services included.

In this post, we discuss a number of techniques to enable efficient memory management for Apache Spark applications when reading data from Amazon S3 and compatible databases using a JDBC connector. We describe how Glue ETL jobs can utilize the partitioning information available from AWS Glue Data Catalog to prune large datasets, manage large number of small files, and use JDBC optimizations for partitioned reads and batch record fetch from databases.  You can use some or all of these techniques to help ensure your ETL jobs perform well.

This post demonstrates how to extend the metadata contained in the Data Catalog with profiling information calculated with an Apache Spark application based on the Amazon Deequ library running on an EMR cluster. You can query the Data Catalog using the AWS CLI. You can also build a reporting system with Athena and Amazon QuickSight to query and visualize the data stored in Amazon S3.

Tens of thousands of customers use Amazon Redshift to power their workloads to enable modern analytics use cases, such as Business Intelligence, predictive analytics, and real-time streaming analytics. As an administrator or data engineer, it’s important that your users, such as data analysts and BI professionals, get optimal performance. You can use the Amazon Redshift […]

RStudio is an integrated development environment (IDE) for R, a language and environment for statistical computing and graphics. As a data scientist, you may integrate R and Spark (a big data processing framework) to analyze large datasets. You can use an R package called sparklyr to offload filtering and aggregation of large datasets from your […]

In this post, we discuss how to leverage the automatic code generation process in AWS Glue ETL to simplify common data manipulation tasks, such as data type conversion and flattening complex structures. We also explore using AWS Glue Workflows to build and orchestrate data pipelines of varying complexity. Lastly, we look at how you can leverage the power of SQL, with the use of AWS Glue ETL and Glue Data Catalog, to query and transform your data.

This post walks you through accessing the AWS COVID-19 data lake through the AWS Glue Data Catalog via Amazon SageMaker or Jupyter and using the open-source AWS Data Wrangler library. AWS Data Wrangler is an open-source Python package that extends the power of Pandas library to AWS and connects DataFrames and AWS data-related services (such as Amazon Redshift, Amazon S3, AWS Glue, Amazon Athena, and Amazon EMR). For more information about what you can build by using this data lake, see the associated public Jupyter notebook on GitHub.

Recently, we helped a large enterprise customer who was building their data warehouse on Amazon Redshift, using Azure AD as a corporate directory. Their requirement was to enable data warehouse users to use their corporate credentials to query data in Redshift. Doing so not only provided a better user experience — users can utilize their […]

Today we are adding a new Amazon Kinesis Data Firehose feature to set up VPC delivery to your Amazon Elasticsearch Service domain from the Kinesis Data Firehose. If you have been managing a custom application on Amazon Kinesis Data Streams to keep traffic private, you can now use Kinesis Data Firehose and load your data […]

Amazon Redshift is the most popular cloud data warehouse because it provides fast insights at a low cost. Customers can confidently run mission critical workloads, even in highly regulated industries, because Amazon Redshift comes with out of the box security and compliance. The security features, combined with the ability to easily analyze data in-place and […]