Analytics

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

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.

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.

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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Think of a data professional (data scientist/data engineer/business analyst/…), and guess what they do all day. Design big data algorithms? Build state-of-the-art, scalable pipelines? Discover insights that drive business growth? Wrong. Data professionals spend over 40% of their time preparing data before they even start using it for their job. The data preparation process is the most time-consuming task in a data operative’s schedule.