AWS Big Data Blog
Category: Analytics
Analyze Your Data on Amazon DynamoDB with Apache Spark
Manjeet Chayel is a Solutions Architect with AWS Every day, tons of customer data is generated, such as website logs, gaming data, advertising data, and streaming videos. Many companies capture this information as it’s generated and process it in real time to understand their customers. Amazon DynamoDB is a fast and flexible NoSQL database service […]
Optimize Spark-Streaming to Efficiently Process Amazon Kinesis Streams
Rahul Bhartia is a Solutions Architect with AWS Martin Schade, a Solutions Architect with AWS, also contributed to this post. Do you use real-time analytics on AWS to quickly extract value from large volumes of data streams? For example, have you built a recommendation engine on clickstream data to personalize content suggestions in real time […]
Introducing On-Demand Pipeline Execution in AWS Data Pipeline
February 2023 Update: Console access to the AWS Data Pipeline service will be removed on April 30, 2023. On this date, you will no longer be able to access AWS Data Pipeline though the console. You will continue to have access to AWS Data Pipeline through the command line interface and API. Please note that […]
Process Amazon Kinesis Aggregated Data with AWS Lambda
Ian Meyers is a Solutions Architecture Senior Manager with AWS Last year, we introduced the Amazon Kinesis Producer Library (KPL) to simplify the development of applications that need to send data to Amazon Kinesis Streams. Many customers use aggregation, which allows you to send multiple records to a single Amazon Kinesis Streams record. Although the […]
Amazon Redshift UDF repository on AWSLabs
Christopher Crosbie is a Healthcare and Life Science Solutions Architect with Amazon Web Services Zach Christopherson, an Amazon Redshift Database Engineer, contributed to this post Did you ever have a need for complex string parsing in Amazon Redshift and wish you could simply add f_parse_url_query_string(url) to your SQL query? Have you ever tried to weigh which would be less […]
Submitting User Applications with spark-submit
Francisco Oliveira is a consultant with AWS Professional Services Customers starting their big data journey often ask for guidelines on how to submit user applications to Spark running on Amazon EMR. For example, customers ask for guidelines on how to size memory and compute resources available to their applications and the best resource allocation model […]
Turning Amazon EMR into a Massive Amazon S3 Processing Engine with Campanile
Michael Wallman is a senior consultant with AWS ProServ Have you ever had to copy a huge Amazon S3 bucket to another account or region? Or create a list based on object name or size? How about mapping a function over millions of objects? Amazon EMR to the rescue! EMR allows you to deploy large […]
Agile Analytics with Amazon Redshift
Nick Corbett is a Big Data Consultant for AWS Professional Services What makes outstanding business intelligence (BI)? It needs to be accurate and up-to-date, but this alone won’t differentiate a solution. Perhaps a better measure is to consider the reaction you get when your latest report or metric is released to the business. Good BI […]
Querying Amazon Kinesis Streams Directly with SQL and Spark Streaming
Amo Abeyaratne is a Big Data consultant with AWS Professional Services Introduction What if you could use your SQL knowledge to discover patterns directly from an incoming stream of data? Streaming analytics is a very popular topic of conversation around big data use cases. These use cases can vary from just accumulating simple web transaction […]
Running an External Zeppelin Instance using S3 Backed Notebooks with Spark on Amazon EMR
Dominic Murphy is an Enterprise Solution Architect with Amazon Web Services Apache Zeppelin is an open source GUI which creates interactive and collaborative notebooks for data exploration using Spark. You can use Scala, Python, SQL (using Spark SQL), or HiveQL to manipulate data and quickly visualize results. Zeppelin notebooks can be shared among several users, […]

