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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.

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DATA

Data wrangling — what it is and why it is important

As data continues to grow in both size and complexity, it is becoming increasingly difficult for organizations to extract valuable insights from it. This is where data wrangling comes in. Data wrangling, which is also known as data munging or data cleaning, is the process of gathering, cleaning, transforming, and preparing unprocessed data into a format that is more easily understood and analyzed. Data wrangling enables organizations to leverage the full potential of their data. In this article, we delve into data wrangling, exploring what it is, why it is important and the key tasks involved in the process.
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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.
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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.
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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.
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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.
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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.

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