For many companies, cloud costs are among the top investments these days. With a growing number of services, instances and regions, cloud cost optimization is becoming increasingly painful. Companies use cloud management platforms to optimize costs and increase cloud visibility and security. But staying on top of AWS budgets requires proficiency, agility and time—especially when any glitch can result in massive cost bleeds.
Cloud for analytics may be the biggest bait and switch in recent history. Until now. Not long ago, cloud was billed as the promised land – a mystical paradise of flexibility, scalability, performance and, best of all, lower costs than we ever thought possible.
Appian, in cooperation with Defense Information Systems Agency (DISA) engineers and the United States Marine Corps, now supports mobile access to Appian applications within the Department of Defense Information Network (DODIN). This enables access to the Appian platform, as well as Appian applications and solutions deployed in a DODIN Cloud-approved enclave (AWS, GovCloud, Azure, etc.) via any DOD-approved iOS or Android mobile device.
How Cloud Computing is evolving alongside Big Data, Analytics, and AI in Financial Services. New technology like Artificial Intelligence (AI), Cloud Computing, big data, and prescriptive analytics are changing the way the Financial Services sector does business. With evolving tech comes both new opportunities as well as different risks, and companies within the space must innovate and embrace new ideas as shifting business conditions and changing consumer preferences dictate new norms.
The recent global pandemic caused by the COVID-19 virus has threatened the sanctity of our humanity and the well-being of our societies at large. Similar to times of war, the pandemic has also given us the opportunity to appreciate the things we take for granted such as health workers, food suppliers, drivers, grocery store clerks and many others who are in the frontlines keeping us safe at this difficult time, Salute!
SQL has long been the universal language for working with data. In fact it’s more relevant today than it was 40 years ago. Many data technologies were born without it and inevitably ended up adopting it later on. Apache Kafka is one of these data technologies. At Lenses.io, we were the first in the market to develop a SQL layer for Kafka (yes, before KSQL) and integrate it in a few different areas of our product for different workloads.
If you’re an engineer exploring a streaming platform like Kafka, chances are you’ve spent some time trying to work out what’s going on with the data in there. But if you’re introducing Kafka to a team of data scientists or developers unfamiliar with its idiosyncrasies, you might have spent days, weeks, months trying to tack on self-service capabilities. We’ve been there.
From data stagnating in warehouses to a growing number of real-time applications, in this article we explain why we need a new class of Data Catalogs: this time for real-time data. The 2010s brought us organizations “doing big data”. Teams were encouraged to dump it into a data lake and leave it for others to harvest. But data lakes soon became data swamps.
Have you ever found yourself copy-pasting the same bits of JavaScript code between different projects? Well, when this situation happens two or three times in a row, it’s usually a good indicator that you have a piece of code that is useful and reusable. So, if you are already reusing your code across several different projects, why not go the extra mile and convert it into a library that allows you to optimize this code-sharing?
