Top 15 Data Engineering Tools in 2023
Analysts require a robust IT ecosystem that offers secure, scalable, and efficient storage. Data engineering professionals interact with analysts to understand what they need. Sometimes, a data architect assists the data engineer in optimizing technical aspects. However, everyone relies on unique techniques and software to deliver the desired results. This post lists the top 15 data engineering tools in 2023.
What is Data Engineering?
Data engineering builds the IT resources for data management, transformation, and smooth retrieval so that analysts and managers can perform their reporting without any problems. Corporate users of data engineering services can leverage consolidated file management and report visualization through cloud-powered data pipeline virtualization.
The stakeholders, like marketers, human resource managers, suppliers, accountants, business strategists, and investors, benefit from data engineers’ work. However, customizing the data processing and report exporting techniques for each business unit demands domain expertise. Moreover, data engineers must know how to utilize multiple software applications and coding languages.
Top 15 Data Engineering Tools in 2023
1| SQL
Data engineers require structured query language (SQL) to operate a relational database management system, i.e., RDBMS. Although the American National Standard Institute (ANSI) standardized it in 1986, this database language has existed since the 1970s. Almost all data strategy services rely on interactive intelligence retrieval interfaces, and SQL is responsible for finding the needed results from central databases.
2| PostgreSQL
Otherwise known as Postgres, this RDBMS language is open source. Being the next iteration of the University of California’s Ingres, it is more flexible and provides high fault tolerance across extensive datasets. PostgreSQL also supports non-relational querying involving JavaScript Object Notation (JSON). Governments, financial institutions, and global firms employ Postgres for object-relational DBMS operations.
3| MATLAB
MathWorks’ MATLAB excels at matrix evaluation, multi-code interfaces, function plotting, and engineering sciences. So, advanced research projects utilize it to develop mathematical models and algorithms addressing real-world design challenges. Today, this tool has become more accessible due to its browser-based graphical user interface (GUI). MATLAB is mobile-friendly, enabling analysts to use their smartphone sensors like accelerometer and global positioning system or GPS for field data collection.
4| PrestoDB
Presto database or PrestoDB allows for efficient database queries because this open-source DBMS language helps data engineering professionals operate freely. They do not need to move data from one location to another to satisfy a user query. Therefore, enterprises like Microsoft, Apple, Netflix, and Airbnb integrate PrestoDB. Doing so assists in cost reduction and data warehousing.
5| Apache Hive
Apache Hive and the Hive Metastore empower data engineers to inspect “data about data,” i.e., metadata. A file’s metadata helps describe its format, creation date, and intended usage. When developing a robust data lake architecture, data engineers require accurate insights into metadata. So, Apache Hive, capable of sorting petabytes of data volume, is useful for data lake management. Since Hive supports incremental data replication, it streamlines database backup and recovery operations.
6| Sisense (Formerly Periscope Data)
Periscope Data has become Sisense for Cloud Data Teams. It provides consistent and unified computing environments facilitating multi-disciplinary collaborations. Data engineers, scientists, strategists, and analysts can benefit from Sisense integration as it can accommodate code-heavy activities. Simultaneously, it uses artificial intelligence for code-free analytics user experiences.
7| BigQuery
Google’s BigQuery is a serverless data warehouse and features a native query processing engine. It can handle terabytes of data volume, requiring a few seconds, while petabytes will take a little longer than a minute. Therefore, it attracts commercial, academic, and policy stakeholders wanting to find insights quickly. BigQuery eliminates the hardware development, management, cybersecurity, and upgrade hurdles thanks to an ad hoc approach toward database queries.
8| Amazon Redshift
It is based on PostgreSQL and delivers simultaneous data retrieval for multiple queries. Redshift’s components include proprietary managed storage, client software packages, clusters, compute nodes, and node slices. Likewise, internal networks and online transaction processing (OLTP) functions enable high-performance analysis, qualifying Redshift as one of the top data engineering tools in 2023.
9| Redash
Redash helps data officers collaborate with multiple stakeholders through accurate data visualization dashboards. Client organizations can also integrate it with MySQL, Postgres, or Redshift. This visual reporting tool assists data engineers and business leaders in telling stories based on analytical insights. Alongside data governance enhancements, Redash provides drag-and-drop dashboard elements to streamline reporting.
10| Python
Python lets data engineering specialists conduct web scraping, data cleansing, transformation, and enrichment. It also features data sampling, visual dashboarding, and insight exploration capabilities. Data strategists and managers can reduce complex codes through Python-powered automation and data pipeline logic. Its libraries comprise TensorFlow, Pyparsing, and Pandas, facilitating creative machine-learning integrations.
11| Snowflake
Snowflake is an enterprise-ready data ingestion and transformation tool that helps data engineers reduce operational costs. Its multi-cluster compute engine also delivers optimized data streaming to maximize data retrieval efficiency. Corporations utilize its Snowgrid, a cross-cloud ecosystem, to improve governance standards and overcome the drawbacks of conventional DBMS.
12| Looker
Looker allows data engineers to link and monitor their data sources in real time. Companies can utilize Looker for organizational business intelligence, data modeling, and departmental self-service DBMS development. Flexible visualization is one of the strengths of Looker, making it essential to all data engineering professionals.
13| Scala
Scala supports object-oriented and functional software development methods and is a general-purpose language. Data engineers must learn how to use Scala as effectively as Python, MongoDB, and SQL to optimize their code for Java and Android platforms. If they want to run a Scala application from clients’ browsers, they will need a Scala.js compiler for JavaScript versions.
14| SAS
Statistical analysis system (SAS) is a computing language serving data analysts, strategists, crime investigators, and weather forecasting professionals. Estimating agricultural resource requirements and crop yields would be its primary objective during its first iteration. Today, SAS and the programs relying on it empower financial institutions to detect fraudulent transactions. If a date engineer wants to work in many industries, SAS-related skills are a must.
15| Tableau
Since 2003, Tableau has been democratizing datasets, visualizing them into user-friendly dashboards, and bringing together professionals in different disciplines. Why does data visualization matter to engineers, managers, investors, and customers? By switching tabulated data into comprehensive dashboards, Tableau eliminates number-crunching, lengthy worksheets, and energy-draining interactions.
Conclusion
These 15 tools dedicated to data engineering, analytics, visualizations, and digital governance assist brands in modernizing their insight extraction methods. They offer specific software packages that let users leverage the latest DBMS technologies.
Some provide data warehousing, while others work excellently irrespective of data storage method, locations, and format. Data engineers must embrace these tools and creatively integrate them into their workflows. They also need to educate their coworkers on how to perform complex tasks using programming languages like Scala and Python.
While a data strategist will conceptualize data operations’ purpose, a team of data architects and engineers will work hard to build the desired IT infrastructure. Therefore, novel tools for data collection, ingestion, transformation, and visualization are gaining momentum worldwide.