19 Data Engineering Tools (With Features And Uses)
Updated 10 March 2023
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Data engineers build systems that collect and manage large volumes of data and convert the raw data into information that companies can interpret and analyse. To stay competitive in this growing field, professionals often use a variety of tools. If you are learning about or considering a career in data engineering, it may be helpful to know which tools tech companies often use. In this article, we discuss what data engineering tools are and list some of the most popular tools.
What are Data Engineering Tools?
Data engineering tools are applications and mechanisms that allow for the collection, storage, transformation and vitalisation of data. Data engineers typically use these tools to manage and perform operations on raw data to convert them into information that is useful and understandable to analysts and other stakeholders. They can then use the information for study purposes or to make business decisions.
Related: Popular Data Mining Tools (Types, Examples And Uses)
19 Popular Data Engineering Tools
There are a wide variety of tools available to data engineering professionals. Some of these have specific uses, while others have broader capabilities. Here are some of the most popular tools:
1. Amazon Redshift
Amazon Redshift is a platform that allows data engineers to build online, cloud-based data warehouses or storage areas to hold their data. They can then run queries on the data within the warehouse to extract insights. Amazon Redshift's simplified query process makes the data accessible to users, such as analysts and business professionals. For increased security, Redshift uses encryption and a customisable firewall. Redshift is popular for its relatively low price and user-friendly interface.
Related: Top 20 Big Data Tools: Big Data And Types Of Big Data Jobs
BigQuery is a warehouse platform that, similarly to Redshift, allows for the storage and analysis of data. It uses machine learning and is capable of predictive modelling. A spreadsheet interface allows users to analyse data using familiar tools. BigQuery automatically backs up data and maintains a recent change history. It also offers advanced security features. Companies and professionals who use other Google tools often also use BigQuery, as it can be fairly intuitive. Its scalability and flexible pricing structure also make it a popular option.
Related: What Is Machine Learning? (Skills, Jobs And Salaries)
Tableau is a platform that allows engineers to combine and analyse data from a variety of sources. It uses a dashboard system to organise and visualise data. Tableau's design focuses on ease of use, making it a popular option for teams comprising a variety of professionals with different knowledge profiles. Tableau is one of the older tools for data engineering, and its familiarity and track record are factors in its popularity.
Similar to Tableau, Looker uses a dashboard interface to visualise data. It is also one of the older tools, so it derives some of its popularity from its familiarity. Google acquired Looker in 2020, and it operates as part of Google Cloud. It primarily focuses on analytics and uses a simple modelling language so users can use it without an in-depth understanding of Structured Query Language (SQL).
Related: Java Vs Python: Key Differences And Similarities
5. Apache Spark
Apache Spark is an open-source engine that is capable of large-scale analytics. It utilises multiple query and programming languages, making it a versatile option for data engineering. Machine learning and SQL analytics typically provide speed and scalability.
6. Apache Hive
Apache Hive is a data warehouse program that is useful for managing very large datasets in a distributed storage format. Apache Hive uses SQL, so it may be a better option for teams that include technical members. A unique aspect of Apache Hive is that it is a volunteer-run, open-source project.
Segment is a platform that works with a data warehouse. You can use it to run queries on existing data or move data from one place to another. Segment specialises in the analysis of customer data and integrates with a variety of customer relationship management (CRM) tools.
Snowflake is a cloud-based data warehousing platform that facilitates data integration and analysis. It offers flexibility, performance and scalability, and is a popular tool for projects that require collaboration. Snowflake's high level of automation supports security and data resilience.
9. Data transformation tools
Data analysts and engineers use DBT to transform data within data warehouses. It focuses mainly on transforming data, rather than extracting data points. DBT can run queries against an existing data warehouse by accepting code and compiling it into SQL queries.
Redash provides dashboards to help the user visualise and share data. Redash supports SQL but also offers other options for querying data. It is an open-source project and maintains a community forum for user support.
Fivetran is a data integration platform that facilitates the transformation and loading of data into data warehouses. It works with a variety of different applications and tools, allowing for project collaboration. Although Fivetran is generally user friendly, its complex design is generally more appropriate for data engineers than for non-technical team members.
12. Great Expectations
Great Expectations is an open-source tool for data validation. It allows data analysts and engineers to document and profile data and aims to deliver quality maintenance and improved collaboration. It has a feature called expectations, which allows you to make and test assertions about your data. This can help you identify errors and test data quality. Great Expectations uses the programming language Python, which may be easier for some people to use.
13. Apache Kafka
Apache Kafka is an open-source system that provides event storage and stream processing. It uses the Java and Scala languages, provides permanent storage, and offers a high level of scalability. Its high throughput allows for messaging between machines at minimal latency. It has built-in event stream processing and is compatible with a wide variety of event sources.
Related: Top 19 Apache Kafka Interview Questions With Sample Answers
14. Power BI
Power BI is a product that has data warehousing capabilities and allows for interactive data visualisation. It focuses on business intelligence (BI) services, combining a variety of components to deliver a range of BI functions. It can be helpful for transforming and connecting data sets.
Stitch is an extraction, transformation and loading platform that can move data from a wide range of sources into a warehouse. It allows users to process data without requiring them to have advanced coding knowledge, so it can be a good option for less technical users. Stitch's main focuses are scalability and performance. It is a popular software option for teams working with sales and marketing data.
Periscope is a data warehouse that uses a dashboard format to provide warehousing, analytics, visualisation and data mining. It uses SQL, Python and R programming languages, but works easily for users with minimal coding knowledge. Periscope's focus is to simplify querying to allow decision-makers to work directly with data, even if they may have a limited understanding of SQL. It is one of the more expensive data tools, but it offers customer support and relative ease of use.
Mode is a collaborative data platform that focuses on BI, data distribution and insights. It uses reports and dashboards to facilitate data visualisation. Additionally, it operates with SQL, Python and R, and incorporates functions that can assist a user with learning SQL or Python. Mode is a free platform and can integrate with a variety of other data tools and databases.
Prefect is an open-source platform that data engineers can use to build, test and run data pipelines. It focuses on workflow management and can offer a good degree of security and scalability. Prefect operates using Python and can integrate with a variety of other tools and platforms.
Presto is a platform that provides a distributed query engine for large-scale data analysis. It uses SQL and can integrate with a wide range of data sources to run queries. One of its unique features is that it can run a single query against multiple data sources at once. Presto focuses on speed and performance but typically works better when users have some understanding of coding, so it often appeals to engineers and analysts.
Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.
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