Category: Learning Levels

Many organizations use external identity providers (IdPs) such as Okta or Microsoft Azure Active Directory to manage their enterprise user identities. These users interact with and run analytical queries across AWS analytics services. To enable them to use the AWS services, their identities from the external IdP are mapped to AWS Identity and Access Management […]

AWS recently announced that Apache Flink is generally available for Amazon EMR on Amazon Elastic Kubernetes Service (EKS). Apache Flink is a scalable, reliable, and efficient data processing framework that handles real-time streaming and batch workloads (but is most commonly used for real-time streaming). Amazon EMR on EKS is a deployment option for Amazon EMR […]

In this post, we show how to build a Q&A bot with RAG (Retrieval Augmented Generation). RAG uses data sources like Amazon Redshift and Amazon OpenSearch Service to retrieve documents that augment the LLM prompt. For getting data from Amazon Redshift, we use the Anthropic Claude 2.0 on Amazon Bedrock, summarizing the final response based on pre-defined prompt template libraries from LangChain. To get data from Amazon OpenSearch Service, we chunk, and convert the source data chunks to vectors using Amazon Titan Text Embeddings model.

Welcome back to our exciting exploration of architectural patterns for real-time analytics with Amazon Kinesis Data Streams! In this fast-paced world, Kinesis Data Streams stands out as a versatile and robust solution to tackle a wide range of use cases with real-time data, from dashboarding to powering artificial intelligence (AI) applications. In this series, we […]

Apache Spark is a powerful big data engine used for large-scale data analytics. Its in-memory computing makes it great for iterative algorithms and interactive queries. You can use Apache Spark to process streaming data from a variety of streaming sources, including Amazon Kinesis Data Streams for use cases like clickstream analysis, fraud detection, and more. Kinesis Data Streams is a serverless streaming data service that makes it straightforward to capture, process, and store data streams at any scale.

With the new open source Amazon Kinesis Data Streams Connector for Spark Structured Streaming, you can use the newer Spark Data Sources API. It also supports enhanced fan-out for dedicated read throughput and faster stream processing. In this post, we deep dive into the internal details of the connector and show you how to use it to consume and produce records from and to Kinesis Data Streams using Amazon EMR.

In this post, we show how to create an AWS Glue job that measures and monitors the data quality of a data pipeline using dynamic rules. We also show how to take action based on the data quality results.

In this post, we explore how to use Zingg’s entity resolution capabilities within an AWS Glue notebook, which you can later run as an extract, transform, and load (ETL) job. By integrating Zingg in your notebooks or ETL jobs, you can effectively address data governance challenges and provide consistent and accurate data across your organization.

As data analytics use cases grow, factors of scalability and concurrency become crucial for businesses. Your analytic solution architecture should be able to handle large data volumes at high concurrency and without compromising speed, thereby delivering a scalable high-performance analytics environment. Amazon Redshift Serverless provides a fully managed, petabyte-scale, auto scaling cloud data warehouse to […]

Data governance is a key enabler for teams adopting a data-driven culture and operational model to drive innovation with data. Amazon DataZone is a fully managed data management service that makes it faster and easier for customers to catalog, discover, share, and govern data stored across Amazon Web Services (AWS), on premises, and on third-party […]

In today’s data-driven landscape, the efficiency and accessibility of querying tools play a crucial role in driving businesses forward. Amazon Redshift recently announced integration with Visual Studio Code (), an action that transforms the way data practitioners engage with Amazon Redshift and reshapes your interactions and practices in data management. This innovation not only unlocks […]