Tag: ELT
Jan 19, 2021 |
If you want to make good data-driven decisions, it’s critical for your data to be as accurate as possible. But ensuring the accuracy of data can be a difficult task in large enterprise environments with thousands of combinations of data sources, transformations, tables, and reports. Read more…
Jan 8, 2021 |
The great migration of data into the cloud didn’t start in 2020, but it certainly accelerated throughout the year. And according to a new survey from IDG, the overwhelming majority of companies are planning to expand their investments in cloud data warehouses and data lakes in 2021. Read more…
Dec 7, 2020 |
Amazon Web Services wants you to create data silos to ensure you get the best performance when processing data. AWS also wants to help unify your data to ensure that insights don’t fall between the cracks. Read more…
Nov 17, 2020 |
Customers running atop Snowflake’s cloud data warehouse soon will find new functionality, including the ability to build ETL data pipelines in Python, as well as the ability to expose pre-built analytic routines as data services. Read more…
Sep 14, 2020 |
Fivetran wants to make it “stupidly simple” for customers to load data into cloud data warehouses, and judging from the company’s rapid growth, it seems to be working. Last week, the extract, transformation, and load (ETL) service company doubled-down on the simplicity mantra with a new plan that lets data customers pay by the sip. Read more…
Nov 7, 2019 |
Looker is best known as a business intelligence platform, which it definitely is. But with today’s release of Looker 7, the company is making a strong case that it’s much more than that. Read more…
Sep 3, 2019 |
Despite the advances we’ve made in data science and advanced analytics in recent years, many projects still are beholden to a technological holdover from the 1980s: extract, transform, and load, or ETL. Read more…
Aug 8, 2019 |
Many of today’s digital transformation and omnichannel customer experience initiatives demand real-time analysis of data. For example, banks need to analyze transactions across their systems in real time to detect and prevent fraud. Read more…
Aug 6, 2019 |
So you’re collecting lots of data with the intention to automate decision-making through the strategic use of machine learning. That’s great! But as your data scientists and data engineers quickly realize, building a production AI system is a lot easier said than done, and there are many steps to master before you get that ML magic. Read more…
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