Companies realize that in order to grow, connect products and services, or protect their business, they need to become data-driven. In selecting the tools to realize these goals, organizations effectively have two choices: a self-selected combination of analytics tools and applications or a unified platform that handles all. In this blog we will discuss the challenges of the former choice that will provide justification for the latter.
One thing nearly all such data providers have is a REST API. Snowflake’s recently announced external functions capability allows Snowflake accounts to call external APIs. By using external functions, data enrichment providers can fulfill requests for data from Snowflake Data Marketplace consumers.
After Alooma announced it was sunsetting its services for Redshift customers, Aceable moved to Fivetran for data integration. In one week, the business integrated all of its sources, including MongoDB — a project that was never completed with Alooma. With Fivetran, Aceable eliminates the need for back-end maintenance and adds Jira to its stack to track project progress across the entire org.
To follow up on my previous article, Guide to Testing Automation with Robot Framework, I will go into more detail about the overall process of writing automated Robot Framework test cases and how to track automation in Jira using Xray. Test automation may seem hard, but it gets easier with the right tools and an open and helpful community like the one surrounding Robot Framework. If your team is using Jira, you can easily integrate testing results coming from the CI/CD pipeline.
Erlang & Elixir are ready for asynchronous work right off the bat. Generally speaking, background job systems aren’t needed as much as in other ecosystems but they still have their place for particular use cases. This post goes through a few best practices I often try to think of in advance when writing background jobs, so that I don’t hit some of the pain points that have hurt me multiple times in the past.
An SCM such as Git is more than just a database for source code. It’s not only the thing you need to interact with to get code to production, but also a log of changes on a project. It’s not just the last couple of weeks of commits that are worth looking at. Any commit remains relevant weeks, months and years later. A commit serves multiple purposes. The first one is to explain a change during its review and the second is to explain a change to a future reader.
Text encoding is fundamental to programming. Web sites, user data, and even the code we write are all text. When encoding breaks, it can feel like the floor is falling out from under you. You're cast into a dimension of bitmasks and codepoints. Logs and backtraces are useless. You consider trading your text editor for a hex editor. But there's hope! In this article, Jose Manuél will show us how encoding errors happen, how they're expressed in Ruby, and how to troubleshoot them.
Apache Hadoop-Ozone is a new-era object storage solution for Big Data platform. It is scalable with strong consistency. Ozone uses Raft protocol, implemented by Apache Ratis (Incubating), to achieve high availability in its distributed system. My team in Tencent started to introduce Ozone as a backend object storage in production a few months ago and we’re onboarding more and more data warehouse users.
