AWS Big Data Blog

Category: Analytics

Automate Amazon ES synonym file updates

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Search engines provide the means to retrieve relevant content from a collection of content. However, this can be challenging if certain exact words aren’t entered. You need to find the right item from a catalog of products, or the correct […]

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Build and optimize real-time stream processing pipeline with Amazon Kinesis Data Analytics for Apache Flink, Part 2

In Part 1 of this series, you learned how to calibrate Amazon Kinesis Data Streams stream and Apache Flink application deployed in Amazon Kinesis Data Analytics for tuning Kinesis Processing Units (KPUs) to achieve higher performance. Although the collection, processing, and analysis of spiky data stream in real time is crucial, reacting to the spiky […]

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Build and optimize a real-time stream processing pipeline with Amazon Kinesis Data Analytics for Apache Flink, Part 1

In real-time stream processing, it becomes critical to collect, process, and analyze high-velocity real-time data to provide timely insights and react quickly to new information. Streaming data velocity could be unpredictable, and volume could spike based on user demand at a given time of day. Real-time analysis needs to handle the data spike, because any […]

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Data preparation using Amazon Redshift with AWS Glue DataBrew

With AWS Glue DataBrew, data analysts and data scientists can easily access and visually explore any amount of data across their organization directly from their Amazon Simple Storage Service (Amazon S3) data lake, Amazon Redshift data warehouse, Amazon Aurora, and other Amazon Relational Database Service (Amazon RDS) databases. You can choose from over 250 built-in functions to merge, pivot, and transpose […]

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real-time streaming data analytics architecture

Build a real-time streaming analytics pipeline with the AWS CDK

A recurring business problem is achieving the ability to capture data in near-real time to act upon any significant event close to the moment it happens. For example, you may want to tap into a data stream and monitor any anomalies that need to be addressed immediately rather than during a nightly batch. Building these […]

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Effective data lakes using AWS Lake Formation, Part 4: Implementing cell-level and row-level security

We announced the general availability of AWS Lake Formation transactions, cell-level and row-level security, and acceleration at AWS re: Invent 2021. In Parts 1 , 2, and 3 of this series, we explained how to set up governed tables, add streaming and batch data to them, and use ACID transactions. In this post, we focus […]

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Work with semistructured data using Amazon Redshift SUPER

With the new SUPER data type and the PartiQL language, Amazon Redshift expands data warehouse capabilities to natively ingest, store, transform, and analyze semi-structured data. Semi-structured data (such as weblogs and sensor data) fall under the category of data that doesn’t conform to a rigid schema expected in relational databases. It often contain complex values […]

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Increase Amazon Elasticsearch Service performance by upgrading to Graviton2

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Amazon OpenSearch Service (successor to Amazon Elasticsearch Service) supports multiple instance types based on your use case. In 2021, AWS announced general purpose (M6g), compute optimized (C6g), and memory optimized (R6g, R6gd) instance types for Amazon OpenSearch Service version 7.9 […]

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Design patterns for an enterprise data lake using AWS Lake Formation cross-account access

In this post, we briefly walk through the most common design patterns adapted by enterprises to build lake house solutions to support their business agility in a multi-tenant model using the AWS Lake Formation cross-account feature to enable a multi-account strategy for line of business (LOB) accounts to produce and consume data from your data […]

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Streaming Amazon DynamoDB data into a centralized data lake

For organizations moving towards a serverless microservice approach, Amazon DynamoDB has become a preferred backend database due to its fully managed, multi-Region, multi-active durability with built-in security controls, backup and restore, and in-memory caching for internet-scale application. , which you can then use to derive near-real-time business insights. The data lake provides capabilities to business teams to plug in […]

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