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In the data landscape, the people are represented by two separate yet equally important groups. The data engineers who design the Lego blocks and the data scientists who build something extraordinary out of them. These are their stories. DUN DUN! And we’re back! Last time, we went over the toolkit needed to get your foot in the door as a data engineer. You’ve gotten over the first hurdle, but I hope you haven’t fallen prey to the Dunning-Kruger Effect.

Self-service analytics is fast becoming a necessity, not a luxury, in the modern enterprise. More businesses want to provide staff with self-service BI tools they can all use, without needing IT help or technical knowledge. This helps drive a data-driven culture across the organization, open up access to data to more people, and unlock actionable insights.

For the past 30 years, the primary data source for business intelligence (BI) and data visualization tools has generally been either a data warehouse or a data mart. But as enterprises today struggle to cope with the growing complexity, scale, and speed of data, it’s becoming clear that the data tools of 30 years ago weren’t designed to handle the enterprise data management challenges of today - especially with the growing variety and amounts of data that enterprises are generating.

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

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.

See how analyzing your business performance and relying on data can help you make better decisions and optimize your business operations.

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 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.

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.