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Data engineering focuses on data architecture, data ingestion, data transformation, and data integration to ensure that data is accessible, reliable, and ready for analysis.

Thumbnail of an article about Data lake vs. data warehouse — differences in data management
DATA

Data lake vs. data warehouse — differences in data management

The day-to-day activities of any organization bring in a lot of information. This data needs to be tracked, stored, and eventually analyzed to allow the business to learn and grow. The bigger the organization, the more data it has, so where a simple database used to be enough for data storage, increasingly often new solutions are required. When it comes to managing big data, i.e. vast amounts of data from multiple data sources in multiple formats, two solutions have become very popular over the years: data warehouses and data lakes.
Maciej Manturewicz

Maciej Manturewicz

Thumbnail of an article about ETL vs. ELT — What are they and when do we need them?
DATA

ETL vs. ELT — What are they and when do we need them?

When we talk about data warehousing, we think of databases, which is only half of the solution. Data must be stored - this is why we need a database. But data must also be delivered into the database from a source. This is where ETL/ELT comes into the equation. Let's briefly explain what a data warehouse is and why we need one. It is a system used for storing and reporting data. Due to their design, data warehouses can process huge amounts of data, making lots of information available for business analytics.
Arkadiusz Kordos

Arkadiusz Kordos

Thumbnail of an article about What is data engineering?
DATA

What is data engineering?

In the past two decades, the digital world has been flooded with data. Most companies have completed digital transformations, and more and more users leave their digital footprint behind every day. All of this information can be turned into useful knowledge that allows companies to make data-driven decisions. This is where the process of data engineering takes place. Data engineering is a branch of data science. Data science is a broad term that includes all of the tasks that make data valuable - from gathering the data to storing, analyzing, and visualizing it.
Maciej Manturewicz

Maciej Manturewicz

Thumbnail of an article about SQL vs. NoSQL. The evolution of relational and non-relational databases
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SQL vs. NoSQL. The evolution of relational and non-relational databases

Databases are tools used daily by companies of all kinds. They make storing and transforming large amounts of data possible, and they are necessary for any data-driven business. But they had to come a long way before they became the databases we know today. Let’s take a look at the history of databases and learn how and why SQL and NoSQL have come to life. Since we, as humans, started collecting data, we also needed a way to store it. So naturally, the concept of a database came before computers. In the computer world, databases came into existence in the early 60s, when in 1962, the Oxford Dictionary first recognized the term “database” as a software system that stores and retrieves data.
Multiple authors

Multiple authors

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DATA

Top 20 data engineering software and tools

Big data is an important part of operations for every technology-focused enterprise. But to make the analytics effective, there needs to be an efficient data management system governed by data engineers. Data pipelines and architecture are complex environments, and building them requires special tools. These include software for collecting, storing, and validating data, as well as applications for data visualization, analytics, and many more. Read on to check out our choices for the top 20 data engineering technologies.
Maciej Manturewicz

Maciej Manturewicz

Thumbnail of an article about Big data. What is it and why is it so important?
DATA

Big data. What is it and why is it so important?

Data analytics is a concept that has been around for decades. Bankers and traders of the past used handwritten spreadsheets to analyze client behavior and predict market trends. But as the years have passed, technology development has provided us with a more thorough and efficient take on data analytics. We’ve come to the age of big data, which has become one of the most significant issues for businesses of all kinds. But why is it so important? Read on to find out why you should care about big data analytics.
Maciej Manturewicz

Maciej Manturewicz

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