Top Data Engineering Technologies in 2021

In today's world, it is hard to imagine any industry that has not been transformed by data science. Every step of our digital day-to-day is the most valuable information a company has to make business decisions. Let's dive into the most popular and useful tools Data Engineers use to manage the right kind of data at the right time.

All of our digital selves are part of data science. We open our email to find custom discounts, turn to our favorite search engine for immediate answers to our questions, and we certainly depend on banks, governments, and companies to identify and mitigate any potential fraud activity regarding our private data.

Today, data is at the center of every industry. Companies use it to answer questions about the business, their customers, products, and chain of production. A company may use many different systems to collect and generate data, and each system typically uses a different technology.

Precisely because of this, data analysis is sometimes challenging and needs experts to perform it: the data is managed and stored in various structures.

Data engineering is designed to support the process, making it possible for consumers of data, such as analysts, data scientists, and executives to reliably, quickly, and securely inspect all of the data available.

Why is Data Engineering growing so fast?

Data is growing faster than ever before. 90% of the data that exists today has been created in the last two years.

Companies are finding more ways to benefit from data, like predicting future sales, shape their customers and market, prevent threats and build new kinds of products. 

As data becomes more complex, this role will continue to grow in importance. And as the demands for data increase, data engineering will become even more critical.

Data is more valuable to companies. Sales, marketing, finance, and others areas of the business are using data to improve strategic decisions.

The technologies used for data are more complex and in need of experts to manage it.

Data Engineering Skills

Data engineering requires a broad set of skills ranging from programming to database design and system architecture. In any of these disciplines, engineers will find different tools and technologies, so they will need some -in not many- of these skills to work their magic:

  • Data processing and ETL/ELT techniques
  • Knowledge of Python, SQL, and Linux
  • Cluster management, data visualization, batch processing, and machine learning
  • Proficiency in report and dashboard creation

A good data engineer will anticipate data scientists’ questions and how they might want to present data and will ensure that the most pertinent data is reliable, transformed, and ready to use. This would be impossible without the tools designed to achieve the best results possible for data management. 

Let’s dive into the most commonly used software tools and programming languages in Data Engineering:

ETL TOOLS

Extract Transform Load (ETL) is a category of technologies that move data between systems. It accesses data from many different sources and then applies rules to cleanse the data so that it is ready for analysis. Examples of ETL products include Informatica and SAP Data Services.

PYTHON

Python is a general-purpose, high-level programming language for web applications, and it's listed in about 70% of all job descriptions for Data Engineers.
Python has become a popular tool for performing ETL tasks, as it's very easy to learn and use and has extensive libraries for accessing databases and storage technologies. Many Data Engineers use Python instead of an ETL tool, as it's more flexible and more powerful for these particular tasks.

SQL 

Structured Query Language (SQL) is the industry standard when it comes to creating, manipulating, and querying data in relational databases.
It's one of the key tools used to create business logic models, execute complex queries, extract key performance metrics, and build reusable data structures. SQL is especially useful when the data source and destination are the same types of database. Sometimes SQL can be relatively slow and memory intensive, but its portability and ease of use make it the weapon of choice for Data Engineers.

MongoDB (or NoSQL)

MongoDB is a popular, open-source Database Management System (DBMS), and uses a document-oriented database model. As a simple, dynamic, and scalable database, the motivation behind the language is to allow you to implement a high performance, high availability, and automatic scaling data system. It is a very easy-to-use system, an extremely flexible one too, and it stores and queries both structured and unstructured data at a high scale.

MongoDB is an excellent choice for processing huge data volumes that preserves data functionality while allowing horizontal scale.

PostgreSQL

PostgreSQL is the most popular open-source relational database in the industry, as Data Engineers find it easy to do analysis using PostgreSQL reporting tools. One of the reasons for PostgreSQL’s reputation is its active open-source community and tools, like DBMS or MySQL.

PostgreSQL is built using an object-relational model. it's lightweight, deeply flexible, very capable, and it offers a wide range of built-in and user-defined functions, as well as extensive data capacity and trusted data integrity.

Apache Spark

Spark is an open-source analytics data processing engine that can quickly perform processing tasks on large-scale data sets, and can also distribute data processing tasks across multiple computers. These are key qualities to big data and machine learning multiverses, which usually require massive computing power to work through large data stores.

Apache Spark represents a popular implementation of Stream Processing. Industries today understand the value of capturing data and making it available within the organization quickly. Stream Processing allows you to query continuous data streams in real-time, such as sensor data, user activity, data from IoT devices, financial trade data, and much more.

Spark supports multiple programming languages, such as Java, R, Scala, and Python.

Apache Kafka

Kafka is an open-source event streaming platform with multiple applications, such as messaging, data synchronization, real-time data streaming, and much more. It's a popular tool for building ELT pipelines and it's widely used as a data collection and ingestion tool.

Apache Kafka is fundamentally used to build real-time streaming data apps that adapt to those streams. Data that is continuously produced by thousands of sources, which typically work simultaneously. Kafka can stream large amounts of data into a target in a fast, reliable way, and it's a scalable high-performance tool.

Amazon Redshift

Amazon Redshift is a fully managed cloud warehouse built by Amazon. It's an easy-to-use cloud warehouse that powers thousands of businesses. This tool allows anyone to easily set up your data warehouse and scales as you grow.

Redshift is an excellent example of a data warehouse that has evolved beyond the data storage classic role. This is a fully managed cloud-based data warehouse designed for large-scale data storage and analysis.

Snowflake

This is a popular cloud-based data warehousing platform that allows businesses to have separate storage and compute options, support for third-party tools, data cloning, and more. Snowflake helps streamline data engineering actions by easily ingesting, transforming, and delivering data for deeper insights.

With this tool, Data Engineers don't have to worry about managing infrastructure, and can focus on other valuable activities for delivering your data. Its unique shared data architecture delivers the performance, scale, elasticity, and concurrency that any today’s organization requires.

HDFS and Amazon S3

Data Engineering uses HDFS or Amazon S3 to store data during processing. HDFS and Amazon S3 are specialized file systems that can store a basically unlimited amount of data, making them valuable for data science tasks.

They are also low-priced, which is important as processing generates large volumes of data. These data storage systems are fully integrated into environments where the data will be processed, which makes managing data systems much easier.

The Data Engineering landscape evolves quickly, expanding the tools used to create pipelines and integrating multiple data sources into a single data warehouse or data lake. These tools are essential to Data professionals, making the job of managing data that need to be aggregated, stored, and analyzed quite a bit easier than it would be without these technologies.

In this data age, it’s necessary to have someone on your team who can give added value to all the information that is generated every day.

Our Data Engineers are highly experienced in the latest data management trends, which has enabled several of our clients to grow and increase their sales through data analysis.

Tell us about your project and we’ll advice you at no cost.