Never before has data become so prevalent in everything we do. Sorting out the best way to make sense of incoming terabytes of data has turned into an extreme sport. Likewise, it has become a life-or-death decision in every organization, regardless of their level of maturity, to determine an analytics strategy to harness the potential power of all that data without running the risk of overwhelming teams and paralyzing processes.
Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.
Every large enterprise organization is attempting to accelerate their digital transformation strategies to engage with their customers in a more personalized, relevant, and dynamic way. The ability to perform analytics on data as it is created and collected (a.k.a. real-time data streams) and generate immediate insights for faster decision making provides a competitive edge for organizations.
Caching is essential for high-performance web apps; luckily, Python and Django provide excellent tools to help. In this article, Muhammed Ali walks through some popular caching techniques.
Right before WWDC 2022, Bitrise announced the first scalable virtualized Apple Silicon M1 CI/CD dev environment. Then Apple announced M2 for iOS, and everyone wonders, is M2 in the cloud happening?
For many of us, the word ‘Zapier’ has become interchangeable with the word ‘automation’. They are undoubtedly the most prominent and loved integration service out there. Yet it doesn’t mean that Zapier is the best option for everyone, forever. If you are on this page right now, it means you’ve been searching for Zapier alternatives because it just stopped working for you the way it used to.
