AWS Big Data Blog

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 […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Today we are adding a new Amazon Kinesis Data Firehose feature to set up VPC delivery to your Amazon OpenSearch Service (successor to Amazon Elasticsearch Service) domain from the Kinesis Data Firehose. If you have been managing a custom application […]

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 […]

The Amazon Redshift materialized views function helps you achieve significantly faster query performance on repeated or predictable workloads such as dashboard queries from Business Intelligence (BI) tools, such as Amazon QuickSight. It also speeds up and simplifies extract, load, and transform (ELT) data processing. You can use materialized views to store frequently used precomputations and […]

This post showcases TruFactor Intelligence-as-a-Service data on AWS Data Exchange. TruFactor’s anonymization platform and proprietary AI ingests, filters, and transforms more than 85 billion high-quality raw signals daily from wireless carriers, OEMs, and mobile apps into a unified phygital consumer graph across physical and digital dimensions. TruFactor intelligence is application-ready for use within any AWS analytics or ML service to power your models and applications running on AWS, with no additional processing required.