Tag: IT data analytics

Customers are looking for ways to securely and cost-efficiently manage large volumes of sensitive data archival and deletion in their data lake by following regulations and data protection and privacy laws, such as GDPR, POPIA, and LGPD.

For decades, enterprises used online analytical processing (OLAP) workloads to answer complex questions about their business by filtering and aggregating their data. These complex queries were compute and memory-intensive. This required teams to build and maintain complex extract, transform, and load (ETL) pipelines to model and organize data, oftentimes with commercial-grade analytics tools.

Organizations today want to make data-driven decisions. The data could lie in multiple source systems, such as line of business applications, log files, connected devices, social media, and many more. As organizations adopt software as a service (SaaS) applications, data becomes increasingly fragmented and trapped in different “data islands.” To make decision-making easier, organizations are building data lakes, which is a centralized repository that allows you to store all your structured and unstructured data at any scale.

While creating data lakes on the cloud, the data catalog is crucial to centralize metadata and make the data visible, searchable, and queryable for users. With the recent exponential growth of data volume, it becomes much more important to optimize data layout and maintain the metadata on cloud storage to keep the value of data lakes

Maintaining customer data privacy, protection against intellectual property loss, and compliance with data protection laws are essential objectives of today’s organizations. To protect data against security threats, vulnerabilities within the organization, malicious software, or cyber criminality, organizations are increasingly encrypting their data.

This is a guest blog post co-written by Rajagopal Mahendran, Development Manager at the Optus IT Innovation Team. Optus is part of The Singtel group, which operates in one of the world’s fastest growing and most dynamic regions, with a presence in 21 countries.

With Amazon Redshift , you can use SQL to query and combine exabytes of structured and semi-structured data across your data warehouse, operational databases , and data lake . Now that AQUA (Advanced Query Accelerator) is generally available , you can improve the performance of your queries by up to 10 times with no additional costs and no code changes.

The best way to get timely insights and react quickly to new information you receive from your business and your applications is to analyze streaming data . This is data that must usually be processed sequentially and incrementally on a record-by-record basis or over sliding time windows, and can be used for a variety of analytics including correlations, aggregations, filtering, and sampling.

Enterprises are struggling to make high quality data easily discoverable and accessible for analytics, across multiple silos, to a growing number of people and tools within their organization. They are often forced to make tradeoffs—to move and duplicate data across silos to enable diverse analytics use cases or leave their data distributed but limit the agility of decisions.

Many useful business insights can arise from analyzing customer preferences, behavior, and usage patterns. With this information, businesses can innovate faster and improve the customer experience, leading to better engagement and accelerating product adoption.

An integral part of DevOps is adopting the culture of continuous integration and continuous delivery (CI/CD). This enables teams to securely store and version code, maintain parity between development and production environments, and achieve end-to-end automation of the release cycle, including building, testing, and deploying to production.

Amazon Customer Service solves exciting and challenging customer care problems for Amazon.com, the world’s largest online retailer. In 2021, the Amazon Customer Service Technology team upgraded its dense-compute nodes (dc2.8xlarge) to the Amazon Redshift RA3 instance family (ra3.16xlarge).

In this post, we explain how Imperva used Amazon Athena, Amazon SageMaker, and Amazon QuickSight to develop a machine learning (ML) clustering algorithm that can efficiently detect botnets attacking your infrastructure.

Many customers are gathering large amount of data, generated from different sources such as IoT devices, clickstream events from websites, and more. To efficiently extract insights from the data, you have to perform various transformations and apply different business logic on your data.