Get insights and advice on automating repetitive data engineering, optimizing data platform costs, and accelerating data initiatives at your company.
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Unlock actionable insights and drive business growth with efficient, automated data pipelines. Explore the basics, challenges, and new approaches.
Explore the ETL process, its importance, the transformation to ELT, and the tools needed to consolidate and analyze data effectively.
In this episode, Sean and I chat with one of Ascend’s Field Data Engineers, Shayaan Saiyed, about one thing data engineers can’t function without—data transformations.
How do you structure and make data accessible for stakeholders to drive insights? The answer is data transformation. Discover more!
Leading data teams is challenging. Few technological domains have undergone such rapid change over the past few years. Yet the vast majority of data teams, 96% to be exact, are at or over capacity.
Explore the essentials of data ingestion, its types, challenges, and its critical role in effective data analysis and informed decision-making.
As the demand for data engineers increases, data teams must be prepared with the right tools and resources to ensure the role maintains a productive and effective workflow
As we all navigate these uncertain times, one thing has never been more certain — the need to reduce spend. Dollars saved directly equates to employees kept, strategic investments continued, and savings passed on to customers. Finding unnecessary spend and inefficiencies across the business is more important than ever before.
Data scientists and data engineers play the primary role in accelerating a company’s data sophistication, providing both the technology and domain expertise from a sea of zeros and ones into valuable data products. As we reflect on 2019 and look forward to the year and decade ahead, we will examine
Let’s look at how managers of data teams can set the stage for a path that fuels speed and business results, by sorting out different aspects of what needs to get accomplished, and tagging four common mistakes as killer anti-patterns.
Today’s scale of data creation and ingestion has reached magnitudes that have fueled an Icarus-like obsession with data-driven business decisions. The desire for velocity in analytical processing, machine learning, and visualization has only enlarged the gap between the vision of a data-powered intelligence engine and the actual tools used for
While your data team likely includes people with all three of these points of view, what really matters is the position of the leaders, and the pace with which the team is adapting to the real needs of the business. So while we’re rolling the dice with the alphas,
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