AWS Big Data Blog

This blog post presents an architecture solution that allows customers to extract key insights from Amazon S3 access logs at scale. We will partition and format the server access logs with Amazon Web Services (AWS) Glue, a serverless data integration service, to generate a catalog for access logs and create dashboards for insights.

Amazon’s serverless Apache Kafka offering, Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless, is attracting a lot of interest. It’s appreciated for its user-friendly approach, ability to scale automatically, and cost-saving benefits over other Kafka solutions. However, a hurdle encountered by many users is the requirement of MSK Serverless to use AWS Identity and Access Management (IAM) access control. At the time of writing, the Amazon MSK library for IAM is exclusive to Kafka libraries in Java, creating a challenge for users of other programming languages. In this post, we aim to address this issue and present how you can use Amazon API Gateway and AWS Lambda to navigate around this obstacle.

In this post, we show how you can implement a suffix-based search. OpenSearch is an open-source RESTful search engine built on top of the Apache Lucene library. OpenSearch full-text search is fast, can give the result of complex queries within a fraction of a second. With OpenSearch, you can convert unstructured text into structured text using different text analyzers, tokenizers, and filters to improve search. OpenSearch uses a default analyzer, called the standard analyzer, which works well for most use cases out of the box. But for some use cases, it may not work best, and you need to use a specific analyzer.

In this post, we discuss the enhancements to Amazon Redshift stored procedures for non-atomic transaction mode. This mode provides enhanced transaction controls that enable you to automatically commit the statements inside the stored procedure.

Ingesting a high volume of streaming data has been a defining characteristic of operational analytics workloads with Amazon OpenSearch Service. Many of these workloads involve either self-managed Apache Kafka or Amazon Managed Streaming for Apache Kafka (Amazon MSK) to satisfy their data streaming needs. Consuming data from Amazon MSK and writing to OpenSearch Service has been a challenge for customers. AWS Lambda, custom code, Kafka Connect, and Logstash have been used for ingesting this data. These methods involve tools that must be built and maintained. In this post, we introduce Amazon MSK as a source to Amazon OpenSearch Ingestion, a serverless, fully managed, real-time data collector for OpenSearch Service that makes this ingestion even easier.

Amazon Redshift supports querying a wide variety of data formats, such as CSV, JSON, Parquet, and ORC, and table formats like Apache Hudi and Delta. Amazon Redshift also supports querying nested data with complex data types such as struct, array, and map. With this capability, Amazon Redshift extends your petabyte-scale data warehouse to an exabyte-scale data lake on Amazon S3 in a cost-effective manner. Apache Iceberg is the latest table format that is supported by Amazon Redshift. In this post, we show you how to query Iceberg tables using Amazon Redshift, and explore Iceberg support and options.

This post demonstrates how to use Terraform to create, deploy, and clean up OpenSearch Serverless infrastructure.. Amazon OpenSearch Serverless provides the search and analytical functionality of OpenSearch without the manual overhead of configuring, managing, and scaling OpenSearch clusters. It automatically scales the resources based on your workload, and you only pay for the resources consumed. Managing OpenSearch Serverless is simple, but with infrastructure as code (IaC) software like Terraform, you can simplify your resource management even more.

In this post we discuss how we can build and orchestrate in a few steps an ETL process for Amazon Redshift using Amazon S3 Event Notifications for automatic verification of source data upon arrival and notification in specific cases. And we show how to use AWS Step Functions for the orchestration of the data pipeline. It can be considered as a starting point for teams within organizations willing to create and build an event driven data pipeline from data source to data warehouse that will help in tracking each phase and in responding to failures quickly. Alternatively, you can also use Amazon Redshift auto-copy from Amazon S3 to simplify data loading from Amazon S3 into Amazon Redshift.

To improve a Spark application’s efficiency, it’s essential to monitor its performance and behavior. In this post, we demonstrate how to publish detailed Spark metrics from Amazon EMR to Amazon CloudWatch. This will give you the ability to identify bottlenecks while optimizing resource utilization.

Amazon OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service that makes it simple for you to run search and analytics workloads without having to think about infrastructure management. The compute capacity used for data ingestion, and search and query in OpenSearch Serverless is measured in OpenSearch Compute Units (OCUs). Customers can configure […]