Systems | Development | Analytics | API | Testing

While APIs (application programming interfaces) have been around for a while, their importance has grown in recent years. Over this time, APIs have even become necessities in most business operations. Did you know that the global investment in APIs is estimated to be worth around $23.6 million annually? The numbers speak for themselves. APIs are quickly becoming a vital part of every business operation competing in the digital economy.

The data-driven culture cultivated in modern-day organizations is focused on deriving the best possible business insights from their data. With data scattered across the globe, these organizations' most significant challenge is to break the silos of their decentralized data and gather new data for analysis in real-time. To address the data silo problem, data engineering brought forward solutions like ETL, ELT, and data integration tools.

A data warehouse is a centralized storage system for structured data. The data stored here is used for reporting, analytical processing and business intelligence.

For the next interview in our series speaking to technical leaders from around the world, we’ve welcomed Zack Rosenberg, CEO and Co-Founder at CatapultX.

Data exploration is the first step of data analysis. It allows us to uncover how the dataset you are working with looks like: ‍ Data scientists, data engineers, data analysts and business users regularly use data exploration as part of their pipeline to understand data, uncover hidden insights and prepare data for further analysis.

A distributed database can offer benefits and tools to any company’s data storage and analytics processes — as long as they know how to use one.

Data science is vital to business success. It’s our window into the likes and habits of our customers, creating opportunities to glean insights from the mountains of data we collect every day. Data has always helped businesses with decision-making, but AI is taking it a step further. So much so that today it can even be applied to the practice of creating impressive email subject lines. Machine learning for information management is now a key ally for every organization worldwide.

Imagine you’re driving a car. You can see what’s happening on the road in front of you, but you have no idea what’s going on under the hood. It’s like driving blindly without any gauges or a dashboard to give you vital information. You don’t know how fast you’re going, how much fuel you have left, or if something is about to go wrong. In the same way, data engineers who lack data observability are like drivers with a limited view of the road.

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Let's simplify ETL to identify seven criteria that shouldn't be overlooked. Although these requirements seem simple, my purpose is to highlight that ETL requirements will differ if your company is looking at operational data pipelines vs. analytical use cases.