Tag: IT data analytics

Bread, a division of Alliance Data Systems, is a technology-driven payments company that integrates with merchants and partners to personalize payment options for their customers.

The need to derive meaningful and timely insights increases proportionally with the amount of data being collected. Data warehouses play a key role in storing, transforming, and making data easily accessible to enable a wide range of use cases, such as data mining, business intelligence (BI) and reporting, and diagnostics, as well as predictive, prescriptive, and cognitive analysis.

Over the last several years, enterprises have accumulated massive amounts of data. Data volumes have increased at an unprecedented rate, exploding from terabytes to petabytes and sometimes exabytes of data. Increasingly, many enterprises are building highly scalable, available, secure, and flexible data lakes on AWS that can handle extremely large datasets.

Amazon Redshift is the fastest and most widely used cloud data warehouse. Tens of thousands of customers run business-critical workloads on Amazon Redshift. Amazon Redshift offers many features that enable you to build scalable, highly performant, cost-effective, and easy-to-manage workloads.

Amazon Kinesis Data Firehose streams data to AWS destinations such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift , and Amazon OpenSearch Service (successor to Amazon Elasticsearch Service). Additionally, Kinesis Data Firehose supports destinations to third-party partners.

VPC Flow Logs help you understand network traffic patterns, identify security issues, audit usage, and diagnose network connectivity on AWS. Customers often route their VPC flow logs directly to Amazon Simple Storage Service (Amazon S3) for long-term retention.

Extract, transform, and load (ETL) is the process of reading source data, applying transformation rules to this data, and loading it into the target structures. ETL is performed for various reasons. Sometimes ETL helps align source data to target data structures, whereas other times ETL is done to derive business value by cleansing, standardizing, combining, aggregating, and enriching datasets.

This post is co-written with Manoj Gundawar from Viasat. Viasat is a satellite internet service provider based in Carlsbad, CA, with operations across the United States and worldwide. Viasat’s ambition is to be the first truly global, scalable, broadband service provider with a mission to deliver connections that can change the world.

This post is co-written with Madhavan Sriram and Diego Menin from Amazon Transportation Services (ATS). The transportation and logistics industry covers a wide range of services, such as multi-modal transportation, warehousing, fulfillment, freight forwarding, and delivery.

As organizations embark on their data modernization journey, big data analytics and machine learning (ML) use cases are becoming even more integral parts of business. The ease for data preparation and seamless integration with third-party data sources is of paramount importance in order to gain insights quickly and make critical business decisions faster.

Data volumes in organizations are increasing at an unprecedented rate, exploding from terabytes to petabytes and in some cases exabytes. As data volume increases, it attracts more and more users and applications to use the data in many different ways—sometime referred to as data gravity .

This post is co-written with data engineers, Anton Morozov and James Phillips, from Weatherbug. WeatherBug is a brand owned by GroundTruth , based in New York City, that provides location-based advertising solutions to businesses.

With Amazon EMR 5.32 , Amazon EMR introduced Apache Ranger 2.0 support, which allows you to enable authorization and audit capabilities for Apache Spark, Amazon Simple Storage Service (Amazon S3), and Apache Hive. It also enabled authorization audits to be logged in Amazon CloudWatch .

Many businesses have an essential need for structured data stored in their own database for business operations and offerings. For example, a company that produces electronics may want to store a structured dataset of parts. This requires the following properties: color, weight, connector type, and more.