AWS Big Data Blog

In today’s market, business success often lies in the ability to glean accurate insights and predictions from data. However, data scientists and analysts often find that the data they have at their disposal isn’t enough to help them make accurate predictions for their use cases. A variety of factors might alter an outcome and should […]

Many enterprise customers have demanding query throughput requirements for their data warehouses. Some may be able to address these requirements through horizontally or vertically scaling a single cluster. Others may have a short duration where they need extra capacity to handle peaks that can be addressed through Amazon Redshift concurrency scaling. However, enterprises with consistently […]

Data is a strategic asset. Getting timely value from data requires high-performance systems that can deliver performance at scale while keeping costs low. Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. We continue to add new capabilities to […]

In this post, we walk you through how to set up automatic notifications of query monitoring rule (QMR) violations in Amazon Redshift to a Slack channel, so that Amazon Redshift users can take timely action. Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. With Amazon Redshift, you can analyze your […]

Today’s global, data-driven organizations treat data as an asset and use it across different lines of business (LOBs) to drive timely insights and better business decisions. This requires you to seamlessly share and consume live, consistent data as a single source of truth without copying the data, regardless of where LOB users are located. Amazon […]

AWS Glue DataBrew offers over 250 pre-built transformations to automate data preparation tasks (such as filtering anomalies, standardizing formats, and correcting invalid values) that would otherwise require days or weeks writing hand-coded transformations. You can now write cleaned and normalized data directly into JDBC-supported databases and data warehouses without having to move large amounts of […]

AWS Glue is a fully managed serverless service that allows you to process data coming through different data sources at scale. You can use AWS Glue jobs for various use cases such as data ingestion, preprocessing, enrichment, and data integration from different data sources. AWS Glue version 3.0, the latest version of AWS Glue Spark […]

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. […]

Personally identifiable information (PII) data handling is a common requirement when operating a data lake at scale. Businesses often need to mitigate the risk of exposing PII data to the data science team while not hindering the productivity of the team to get to the data they need in order to generate valuable data insights. […]

Amazon Kinesis Data Analytics Studio makes it easy for customers to analyze streaming data in real time, as well as build stream processing applications powered by Apache Flink using standard SQL, Python, and Scala. Just a few clicks in the AWS Management console lets customers launch a serverless notebook to query data streams and get […]