AWS Big Data Blog

Category: Amazon Simple Storage Service (S3)

Build a big data Lambda architecture for batch and real-time analytics using Amazon Redshift

With real-time information about customers, products, and applications in hand, organizations can take action as events happen in their business application. For example, you can prevent financial fraud, deliver personalized offers, and identify and prevent failures before they occur in near real time. Although batch analytics provides abilities to analyze trends and process data at […]

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Simplify your ETL and ML pipelines using the Amazon Athena UNLOAD feature

Many organizations prefer SQL for data preparation because they already have developers for extract, transform, and load (ETL) jobs and analysts preparing data for machine learning (ML) who understand and write SQL queries. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using […]

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Secure data movement across Amazon S3 and Amazon Redshift using role chaining and ASSUMEROLE

Data lakes use a ring of purpose-built data services around a central data lake. Data needs to move between these services and data stores easily and securely. The following are some examples of such services: Amazon Simple Storage Service (Amazon S3), which stores structured, unstructured, and semi-structured data Amazon Redshift, a fully managed, petabyte-scale data […]

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Best practices to optimize data access performance from Amazon EMR and AWS Glue to Amazon S3

Customers are increasingly building data lakes to store data at massive scale in the cloud. It’s common to use distributed computing engines, cloud-native databases, and data warehouses when you want to process and analyze your data in data lakes. Amazon EMR and AWS Glue are two key services you can use for such use cases. […]

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Design patterns: Set up AWS Glue Crawlers using S3 event notifications

The AWS Well-Architected Data Analytics Lens provides a set of guiding principles for analytics applications on AWS. One of the best practices it talks about is build a central Data Catalog to store, share, and track metadata changes. AWS Glue provides a Data Catalog to fulfill this requirement. AWS Glue also provides crawlers that automatically […]

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Diagram depicting a customer's AWS account and an AWS managed AWS account. In the customer account, there is a region box. In the region, there is an Amazon Athena workgroup taking 3 steps. In the first step, the workgroup accesses metadata from the AWS Glue Data Catalog named default. The catalog has a dotted line to an AWS Glue table called amazon_reviews_parquet, which has the attributes and S3 bucket location. The second step from the workgroup queries data from the S3 bucket. The S3 bucket is in the AWS managed AWS account. The bucket is for the Amazon Customer Reviews dataset. In the third step, the workgroup stores the query results in the Amazon S3 bucket in the customer AWS account. The query results can then be read by users with read access to the Athena workgroup.

Improve reusability and security using Amazon Athena parameterized queries

Amazon Athena is a serverless interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL, and you only pay for the amount of data scanned by your queries. If you use SQL to analyze your business on a daily basis, you may find yourself repeatedly […]

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Make data available for analysis in seconds with Upsolver low-code data pipelines, Amazon Redshift Streaming Ingestion, and Amazon Redshift Serverless

Amazon Redshift is the most widely used cloud data warehouse. Amazon Redshift makes it easy and cost-effective to perform analytics on vast amounts of data. Amazon Redshift launched Streaming Ingestion for Amazon Kinesis Data Streams, which enables you to load data into Amazon Redshift with low latency and without having to stage the data in […]

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aws glue blog

Build a serverless pipeline to analyze streaming data using AWS Glue, Apache Hudi, and Amazon S3

Organizations typically accumulate massive volumes of data and continue to generate ever-exceeding data volumes, ranging from terabytes to petabytes and at times to exabytes of data. Such data is usually generated in disparate systems and requires an aggregation into a single location for analysis and insight generation. A data lake architecture allows you to aggregate […]

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Export JSON data to Amazon S3 using Amazon Redshift UNLOAD

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL. Amazon Redshift offers up to three times better price performance than any other cloud data warehouse. Tens of thousands of customers use Amazon Redshift to process exabytes of […]

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Featured Stateful Architecture

Doing more with less: Moving from transactional to stateful batch processing

Amazon processes hundreds of millions of financial transactions each day, including accounts receivable, accounts payable, royalties, amortizations, and remittances, from over a hundred different business entities. All of this data is sent to the eCommerce Financial Integration (eCFI) systems, where they are recorded in the subledger. Ensuring complete financial reconciliation at this scale is critical […]

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