AWS Big Data
OCTOBER 3, 2023
A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. They are the same.
AWS Big Data
MARCH 2, 2023
Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. and later supports the Apache Iceberg framework for data lakes. AWS Glue 3.0 The following diagram illustrates the solution architecture.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Jet Global
SEPTEMBER 4, 2020
There is an established body of practice around creating, managing, and accessing OLAP data (known as “cubes”). Data Lakes. There has been a lot of talk over the past year or two in the D365F&SCM world about “data lakes.” Traditional databases and data warehouses do not lend themselves to that task.
DataKitchen
NOVEMBER 4, 2021
Cloud computing has made it much easier to integrate data sets, but that’s only the beginning. Creating a data lake has become much easier, but that’s only ten percent of the job of delivering analytics to users. It often takes months to progress from a data lake to the final delivery of insights.
DataKitchen
JANUARY 25, 2022
The data requirements of a thriving business are never complete. There is an endless stream of new data sources to integrate, exceptions to manage and requests for new charts, graphs and dashboards. To cope with all of the complexity, the company had to hire more and more consultants each year to engineer and analyze the data.
DataKitchen
JUNE 12, 2023
We had been talking about “Agile Analytic Operations,” “DevOps for Data Teams,” and “Lean Manufacturing For Data,” but the concept was hard to get across and communicate. I spent much time de-categorizing DataOps: we are not discussing ETL, Data Lake, or Data Science.
DataKitchen
JANUARY 3, 2022
Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units. Business analysts sometimes perform data science, but usually, they integrate and visualize data and create reports and dashboards from data supplied by other groups.
Expert insights. Personalized for you.
110 
Let's personalize your content