AWS Big Data Blog
Automating bucketing of streaming data using Amazon Athena and AWS Lambda
In today’s world, data plays a vital role in helping businesses understand and improve their processes and services to reduce cost. You can use several tools to gain insights from your data, such as Amazon Kinesis Data Analytics or open-source frameworks like Structured Streaming and Apache Flink to analyze the data in real time. Alternatively, […]
Read MoreBest practices using AWS SCT and AWS Snowball to migrate from Teradata to Amazon Redshift
This is a guest post from ZS. In their own words, “ZS is a professional services firm that works closely with companies to help develop and deliver products and solutions that drive customer value and company results. ZS engagements involve a blend of technology, consulting, analytics, and operations, and are targeted toward improving the commercial […]
Read MoreBringing the power of embedded analytics to your apps and services with Amazon QuickSight
In the world we live in today, companies need to quickly react to change—and to anticipate it. Customers tell us that their reliance on data has never been greater than what it is today. To improve your decision-making, you have two types of data transformation needs: data agility, the speed at which data turns into […]
Read MoreBuilding an AWS Glue ETL pipeline locally without an AWS account
If you’re new to AWS Glue and looking to understand its transformation capabilities without incurring an added expense, or if you’re simply wondering if AWS Glue ETL is the right tool for your use case and want a holistic view of AWS Glue ETL functions, then please continue reading. In this post, we walk you […]
Read MoreHow to delete user data in an AWS data lake
General Data Protection Regulation (GDPR) is an important aspect of today’s technology world, and processing data in compliance with GDPR is a necessity for those who implement solutions within the AWS public cloud. One article of GDPR is the “right to erasure” or “right to be forgotten” which may require you to implement a solution […]
Read MoreStreaming data from Amazon S3 to Amazon Kinesis Data Streams using AWS DMS
Stream processing is very useful in use cases where we need to detect a problem quickly and improve the outcome based on data, for example production line monitoring or supply chain optimizations. This blog post walks you through process of streaming existing data files and ongoing changes from Amazon Simple Storage Service (Amazon S3) to […]
Read MoreUsing the Amazon Redshift Data API to interact with Amazon Redshift clusters
Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL, and your existing ETL, Business Intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per day and power analytics […]
Read MoreAnalyzing Amazon S3 server access logs using Amazon ES
When you use Amazon Simple Storage Service (Amazon S3) to store corporate data and host websites, you need additional logging to monitor access to your data and the performance of your application. An effective logging solution enhances security and improves the detection of security incidents. With the advent of increased data storage needs, you can […]
Read MoreImplementing LDAP authentication for Hive on a multi-tenant Amazon EMR cluster
As Amazon EMR continues its widespread adoption, it’s important to enforce separation of duties using role-based access when submitting your hive jobs on EMR clusters in multi-tenant environments. In this post, we walk through the steps to set up authentication for Hive using Lightweight Directory Access Protocol (LDAP) and Microsoft Active Directory Domain Controller. Solution […]
Read MoreEnhanced monitoring and automatic scaling for Apache Flink
Thousands of developers use Apache Flink to build streaming applications to transform and analyze data in real time. Apache Flink is an open-source framework and engine for processing data streams. It’s highly available and scalable, delivering high throughput and low latency for the most demanding stream-processing applications. Monitoring and scaling your applications is critical to […]
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