AWS Big Data Blog

Category: Storage

The following diagram shows the flow of our solution.

Integrating Datadog data with AWS using Amazon AppFlow for intelligent monitoring

Infrastructure and operation teams are often challenged with getting a full view into their IT environments to do monitoring and troubleshooting. New monitoring technologies are needed to provide an integrated view of all components of an IT infrastructure and application system. Datadog provides intelligent application and service monitoring by bringing together data from servers, databases, […]

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The following diagram shows the solution architecture for the Vertica custom connector when deployed to AWS.

Querying a Vertica data source in Amazon Athena using the Athena Federated Query SDK

The ability to query data and perform ad hoc analysis across multiple platforms and data stores with a single tool brings immense value to the big data analytical arena. As organizations build out data lakes with increasing volumes of data, there is a growing need to combine that data with large amounts of data in […]

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In the following tree diagram, we’ve outlined what the bucket path may look like as logs are delivered to your S3 bucket

Automating AWS service logs table creation and querying them with Amazon Athena

I was working with a customer who was just getting started using AWS, and they wanted to understand how to query their AWS service logs that were being delivered to Amazon Simple Storage Service (Amazon S3). I introduced them to Amazon Athena, a serverless, interactive query service that allows you to easily analyze data in […]

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Building a cost efficient, petabyte-scale lake house with Amazon S3 lifecycle rules and Amazon Redshift Spectrum: Part 2

In part 1 of this series, we demonstrated building an end-to-end data lifecycle management system integrated with a data lake house implemented on Amazon Simple Storage Service (Amazon S3) with Amazon Redshift and Amazon Redshift Spectrum. In this post, we address the ongoing operation of the solution we built. Data ageing process after a month […]

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Building a cost efficient, petabyte-scale lake house with Amazon S3 lifecycle rules and Amazon Redshift Spectrum: Part 1

The continuous growth of data volumes combined with requirements to implement long-term retention (typically due to specific industry regulations) puts pressure on the storage costs of data warehouse solutions, even for cloud native data warehouse services such as Amazon Redshift. The introduction of the new Amazon Redshift RA3 node types helped in decoupling compute from […]

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The following diagram shows the workflow to connect Apache Airflow to Amazon EMR.

Dream11’s journey to building their Data Highway on AWS

This is a guest post co-authored by Pradip Thoke of Dream11. In their own words, “Dream11, the flagship brand of Dream Sports, is India’s biggest fantasy sports platform, with more than 100 million users. We have infused the latest technologies of analytics, machine learning, social networks, and media technologies to enhance our users’ experience. Dream11 […]

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How FanDuel Group secures personally identifiable information in a data lake using AWS Lake Formation

This post is co-written with Damian Grech from FanDuel FanDuel Group is an innovative sports-tech entertainment company that is changing the way consumers engage with their favorite sports, teams, and leagues. The premier gaming destination in the US, FanDuel Group consists of a portfolio of leading brands across gaming, sports betting, daily fantasy sports, advance-deposit […]

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Working with timestamp with time zone in your Amazon S3-based data lake

With a data lake built on Amazon Simple Storage Service (Amazon S3), you can use the purpose-built analytics services for a range of use cases, from analyzing petabyte-scale datasets to querying the metadata of a single object. AWS analytics services support open file formats such as Parquet, ORC, JSON, Avro, CSV, and more, so it’s […]

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Ingesting Jira data into Amazon S3

Consolidating data from a work management tool like Jira and integrating this data with other data sources like ServiceNow, GitHub, Jenkins, and Time Entry Systems enables end-to-end visibility of different aspects of the software development lifecycle and helps keep your projects on schedule and within budget. Amazon Simple Storage Service (Amazon S3) is an object […]

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Keeping your data lake clean and compliant with Amazon Athena

With the introduction of CTAS support for Amazon Athena (see Use CTAS statements with Amazon Athena to reduce cost and improve performance), you can not only query but also create tables using Athena with the associated data objects stored in Amazon Simple Storage Service (Amazon S3). These tables are often temporary in nature and used […]

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