What are the essential data engineering tools and technologies?

Data engineering plans, builds, and takes care of the data pipelines, systems, and tools that make data analysis, machine learning, and business intelligence possible. To do their jobs well and quickly, data engineers need a wide range of platforms and tools. This post will talk about some of the most important tools and technologies for data engineers that you should know how to use.

What Is Data Engineering?

Data engineering is the process of getting data out of a data warehouse or data lake, changing it, and putting it back in. Data scientists and engineers who know how to use analysis tools to solve problems with big data are usually the ones who do data engineering. 

A data engineer might use a range of platforms and tools to get data from different places, like log files, relational databases, and noSQL databases. After the data is extracted, it can be changed into a different file so that it can be put into a database.

1 Data Storage

One of the most important parts of data engineering is being able to store and handle data in different types of formats, like structured, semi-structured, and unstructured data. You might need different ways to store data, like relational databases, NoSQL databases, data centers, data lakes, or cloud storage, based on the type, amount, and speed of the data. To keep and get to your data, you could use PostgreSQL, MongoDB, Amazon Redshift, Apache Hadoop, or Google Cloud Storage.

2 Data Processing

Another important part of data engineering is being able to change and handle data that comes from different places, like streams, files, APIs, or web scraping. It’s possible that you’ll need to do things like clean, validate, integrate, aggregate, or improve data. You might need tools and platforms for processing data, like Python, SQL, Apache Spark, Apache Kafka, or Apache Airflow, to do this. You can use Python to write scripts that extract, transform, and load (ETL) data from different sources, SQL to query and change data in databases, Apache Spark to handle large amounts of data, Apache Kafka to handle real-time data streams, and Apache Airflow to plan and organize your data pipelines.

3 Data Modeling

Setting up the structure, relationships, and limits of data for a certain use or area is what data modeling is all about. Data modeling is a way to make sure that data is correct, consistent, and useful. This could mean that you need to use ER diagrams, UML diagrams, or data dictionaries to describe the data. You can use ER diagrams to show the entities and links in a relational database, UML diagrams to show the classes and associations in an object-oriented database, and data dictionaries to list the parameters and descriptions of your data elements.

4 Data Analysis

The act of looking into, figuring out, and sharing what you learn from data is called data analysis. Data analysis is a way to find answers, solve problems, and make choices. It’s possible that you will need to use technologies and tools for data analysis, like Matplotlib, R, Jupyter Notebook, or NumPy. As an example, you can use Jupyter Notebook to make interactive notebooks that combine code, text, and visualizations; R to do statistical analysis and data visualization; Pandas to work with and analyze data in tabular format; NumPy to do operations on arrays and do math with numbers; and Matplotlib to turn data into graphs and charts.

5 Data Visualization

Data visualization means showing data in the form of a graph or picture. Visualizing data makes it easier to understand, more interesting, and more useful. Many times, you will need to use tools and technologies like Tableau, Power BI, Plotly, or D3.js to show data. You can use Tableau to make interactive dashboards and reports that show data in different charts, maps, and tables; Power BI to connect and visualize data from different sources and platforms; Plotly to make interactive plots and charts that run on the web; and D3.js to change and display data using HTML, SVG, and CSS.

6 Data Testing

Data testing is the process of making sure that data and data systems are correct, of good quality, and work properly. Data testing is a way to find mistakes, bugs, and strange things in data and data processes and fix them. These are some tools and platforms that you might need to test data: PyTest, SQLTest, Great Expectations, or dbt. PyTest lets you write and run unit tests and integration tests for your Python code. SQLTest lets you write and run SQL queries and assertions for your database. Great Expectations lets you set and check data quality rules and expectations, and DBT lets you test and document your data transformations and models. dbt lets you test and document your data transformations and models with a focus on SQL, making it easier to manage complex data pipelines.

7 Data Cleansing

When it comes to ensuring that the data is of high quality, the process of data purification is among the most critical. Data engineering tools include advanced capabilities that expose abnormalities, inconsistencies, and information that is not connected to the problem itself. These functions are utilized in the process of cleaning data. In order to improve the quality and dependability of the data, which is crucial for analytical models and decision-making processes, it is helpful to perform data cleaning.