Elastic Map Reduce
Nov 5, 2022
# EMR - Amazon Elastic Map Reduce
- Amazon EMR is a manged cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, on AWS to process and analyze vast amounts of data. By using these frameworks and related open-source projects, such as Apache Hive and Apache Pig, you can process data for analytics purposes and business intelligence workloads. Additionally, you can use Amazon EMR to transform and move large amounts of data into and out of other AWS data stores and databases.
- Amazon Redshift is the most widely used cloud data warehouse. It makes it fast, simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. It allows you to run complex analytic queries against terabytes to petabytes of structured and semi-structured data, using sophisticated query optimization, columnar storage on high-performance storage, and massively parallel query execution.
- The key phrases in the scenario are “big data processing frameworks” and “various business intelligence tools and standard SQL queries” to analyze the data. To leverage big data processing frameworks, you need to use Amazon EMR. The cluster will perform data transformations (ETL) and load the processed data into Amazon Redshift for analytic and business intelligence applications.
- Amazon EMR provides a managed Hadoop framework that makes it easy, fast and cost-effective to process vast amounts of data across dynamically scalable Amazon EC2 instances. It securely and reliably handles a broad set of big data use cases, including log analysis, web indexing, data transformations (ETL), machine learning, financial analysis, scientific simulation, and bioinformatics. You can also run other popular distributed frameworks such as Apache Spark, HBase, Presto, and Flink in Amazon EMR, and interact with data in other AWS data stores such as Amazon S3 and Amazon DynamoDB.