When it comes to cloud, being an early adopter does not necessarily put you ahead of the game. I know of companies that have been perpetually “doing cloud” for 10 years, but very few that have “done cloud” in a way that democratises and makes data accessible, with minimal pain points. Cloud is an enabler. It makes it easier to collect, analyse, and disseminate information.
The COVID-19 pandemic has shone a bright light on the need for flexible and engaging leadership. Most of the recent leadership-themed articles focus on how to bridge the remote-only workplace relationship gap, the importance of increased engagement and empathy, and the need to provide technology that empowers and provides a more equitable work/life balance.
Gartner names Fivetran in the 2020 Gartner Magic Quadrant in the data integration tools category. Today, we’re proud to share that Fivetran has been recognized in the August 2020 Gartner Magic Quadrant for Data Integration Tools for the first time. The data integration market is rapidly evolving and expanding as organizations increasingly understand the value of analytics and the importance of data-backed decisions in driving business success.
Fivetran or Stitch for automated ELT? Learn the pros & cons of each platform. October 5, 2020 We commonly hear from our customers that when they initially evaluated cloud data integration solutions, both Fivetran and Stitch came up in their cursory searches. With the Stitch's announcement of their free plan deprecation, we’ve received an influx of questions on how the two solutions differ. Below, we highlight the differences in the approaches to handling automated ELT as well as pricing.
With the pandemic forcing businesses worldwide to reboot, many have no choice but to exact drastic cost-cutting measures to keep the lights on. Cloud computing is an expense incurred by every digital business that, unlike many other operating costs, is largely variable.
Cloudera Data Platform Powered by NVIDIA RAPIDS Software Aims to Dramatically Increase Performance of the Data Lifecycle Across Public and Private Clouds Cloudera announced today a new collaboration with NVIDIA that will help Cloudera customers accelerate data engineering, analytics, machine learning and deep learning performance with the power of NVIDIA GPU computing across public and private clouds.
So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me. Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning performance. Each has his or her role, and they work in sync to merge their actions and fine-tune the results.
This is the second post in a series about data modeling and data governance in the cloud from Snowflake’s partners at erwin. See the first post here. As you move data from legacy systems to a cloud data platform, you need to ensure the quality and overall governance of that data. Until recently, data governance was primarily an IT role that involved cataloging data elements to support search and discovery.
In recent years, organizations have been making massive investments in data analytics to transform their growing volume of data into actionable insights to inform decision-making. However, the pursuit of becoming data-driven has uncovered challenges earlier in the data pipeline that are preventing companies from reaping all the benefits from their data.
