Remove data databases how-to-update-from-select-in-sql-server
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Create Modern SQL Dashboards With Professional Tools & Software

datapine

Companies need to collect, store, monitor, and analyze massive volumes of data in order to manage business performance and successfully deliver profitable results. There is still lots of relational database management included when it comes to online data analysis and different possibilities to perform the same.

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Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

This allows you to simplify security and governance over transactional data lakes by providing access controls at table-, column-, and row-level permissions with your Apache Spark jobs. Many large enterprise companies seek to use their transactional data lake to gain insights and improve decision-making.

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Accelerate your data warehouse migration to Amazon Redshift – Part 7

AWS Big Data

Tens of thousands of customers use Amazon Redshift to gain business insights from their data. With Amazon Redshift, you can use standard SQL to query data across your data warehouse, operational data stores, and data lake. Migrating a data warehouse can be complex.

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Moving Data to an On-Prem Database with Skyvia

Sisense

Many modern businesses choose cloud warehouse services like Google BigQuery or Amazon Redshift to consolidate their data storage and analysis. These services offer the ability to store huge data volumes and process them quickly with massive parallel processing. To replicate your data this way, the first step is to register on Skyvia.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

Building a data platform involves various approaches, each with its unique blend of complexities and solutions. In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

This post is co-written with Ramesh Daddala, Jitendra Kumar Dash and Pavan Kumar Bijja from Bristol Myers Squibb. For the past 5 years, BMS has used a custom framework called Enterprise Data Lake Services (EDLS) to create ETL jobs for business users. BMS’s EDLS platform hosts over 5,000 jobs and is growing at 15% YoY (year over year).