Fivetran partners with Databricks on their launch of Lakehouse for Manufacturing (L4M)

In today’s data-driven world, businesses must deal with complex challenges related to managing, integrating, and properly using massive amounts of data housed in multiple locations. Organizations that unlock the right data architectural approach empower themselves with much better decision-making and strategic insights. Two popular approaches — data mesh and data fabric — have surfaced as prominent and innovative solutions for handling data at scale.

Data has become an essential driver for new monetization initiatives in the financial services industry. With the vast amount of data collected from customers, transactions, and market movements, among other sources, this abundance offers tremendous potential for financial institutions to extract valuable insights that can inform business decisions, improve customer service, and create new revenue streams.

When it comes to picking the right data structure, developers often invest significant time in choosing what should be implemented. A good data structure can simplify data operations, save time, and make code more comprehensive. Collections of data are commonly stored using Objects and Arrays. For storing keys/value pairs, developers use Objects, and for storing indexed lists, they use Arrays. Iterable objects in ECMAScript 2015 presented two new types: Maps and Sets in JavaScript.

Today, all applications run on API. It connects one microservice to another, clients with servers, and runs the most basic yet essential application services. Clearly, a well-oiled API is imperative to ensure the smooth functioning of every system. If API breaks down, the complete user experience and application are put at risk. And only proper API testing can secure the system from such downtime possibilities.

Welcome to the third blog post in our series highlighting Snowflake’s data ingestion capabilities, covering the latest on Snowpipe Streaming (currently in public preview) and how streaming ingestion can accelerate data engineering on Snowflake.

Despite its many advantages, Node.js comes with a set of potential pitfalls if you don't maintain your application properly, such as: In this post, we'll explore these pitfalls and how to avoid them. Let's dive straight in!

Logs are the core of the human-machine interface for software developers and operators. Historically, they are very much like caveman paintings. They were our first attempt to express and understand how our software was working. For several decades, logs were an island of calm in a rapidly changing technological ecosystem. Logs remained the same even as software services became web-based and grew in scale.

How many times have you heard the sentence that starts with “This software is compatible with Windows 8 and above” or “Only compatible with Android version 6.0 and above”? I am sure this would ring a bell for all gamers as games are highly sensitive to the platform they are run on. But for non-gamers too this would be the first thing they need to check if they know they are operating on an older version of the operating system.

Flaky tests are one of the most common and frustrating issues in software testing. These are tests that pass or fail sporadically, even if the code being tested has not changed. This can cause confusion and wasted time for developers who are trying to fix issues that may not even exist.