Thanks to a sudden influx of operations data into our analytics stack, the need for data engineers in the hiring market increased exponentially. To facilitate seamless analytics, data engineers build data pipelines which are essentially the infrastructure designs to enable modern data analytics. Data engineers’ needs are divided into various sets of requirements to build data pipelines. These requirements are met using Data Engineering Tools, which includes a mix of programming languages and data warehouses but are not limited to data management, BI, processing, and analytics tools.
In this blog post, we will be discussing the need for Data Engineering Tools and their importance. We will also share a list of the top 10 data engineering tools for building adequate data infrastructure to support seamless business operations.
What are Data Engineering Tools?
Data Engineering Tools is a composite term used to describe tools that are part of the modern data stack. A modern data stack needs specialized tools that help save engineering time to create data integration. These integrations are cloud-agnostic, end-user-centric, and scalable to meet your growing data needs. In general, data engineering tools help in:
- Building a data pipeline.
- Enabling seamless ETL/ELT operations.
- Producing business intelligence/data visualization reports.
Let’s discuss them briefly, with some examples and levels of importance.
Data Integration: To enable real-time or near-real-time data availability to monitor business requires fully managed ETL tools. Some examples include Fivetran, Hevo Data, Xplenty, and many more.
Data Destination: Cloud data warehouses are next on the list for two reasons: First, it’s an upgrade over on-premise legacy databases. Second, an agile data warehousing solution is perfect for today’s business operations due to its on-the-go scalability and off-the-shelf deployability. Some examples include Amazon Redshift, Google BigQuery, Snowflake, and many more.
Data Transformation: Data transformation is vital because it enables good data analytics. Typically the process of transforming includes converting data from one format to another. Some examples include Adeptia, Hevo Data, Boomi, and many more.
Data Visualization / Business Intelligence: Business intelligence tools are the gateway to answers. BI tools can help businesses make data-informed decisions to mitigate operational risk and attain maximum efficiency in terms of operations enablement. Some examples include Power BI, Tableau, Looker, and many more.
Top 10 Data Engineering Tools
Snowflake
Snowflake, a cloud-based data storage and analytics service provider, is a warehouse-as-a-solution designed to cater to today’s enterprises’ needs. It has built, perfected, and resurrected the data warehouse industry and is genuinely credited for it. It enables users to shift to a cloud-based system quickly. Snowflake’s features include managed infrastructure, on-the-fly scalability, automatic clustering, and ease of integration with ODBC, JDBC, Javascript, Python, Spark, R, and Node.js.
Snowflake’s unique architecture combines the benefits of both shared-disk architecture and shared-nothing architecture; its innovative design has taken full advantage of the cloud. Snowflake’s central data repository has access to data stored in compute nodes while it processes queries using MPP (massively parallel processing) compute clusters. Each node possesses a portion of the entire data set stored locally. Snowflake has a three-layered architecture — that includes Database Store, Query Processing, and Cloud Services.
Amazon Redshift
Redshift is a petabyte-scale data warehouse solution built and designed for data scientists, data analysts, data administrators, and software developers. Its parallel processing and compression algorithms allow users to perform operations on billions of rows, reducing command execution time significantly. Redshift is perfect for analyzing large quantities of data with today’s business intelligence tools in multiple data warehouses.
Amazon Redshift architecture is based on an extensive communication channel between the client application and the data warehouse cluster. These two communicate using the industry-standard JDBC and ODBC drivers for PostgreSQL. The Data warehouse cluster has leader node, compute node, and node slices — these are the core infrastructure components of Redshift’s architecture.
Hevo Data
Hevo allows you to replicate data in near real-time from 150+ Data Sources to the destination of your choice including Snowflake, BigQuery, Redshift, Databricks, and Firebolt. Without writing a single line of code. Finding patterns and opportunities is easier when you don’t have to worry about maintaining the pipelines. So, with Hevo as your data pipeline platform, maintenance is one less thing to worry about.
For the rare times things do go wrong, Hevo ensures zero data loss. To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check Hevo’s in-depth documentation to learn more.
If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent Hevo pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest upto 1 million records.
Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.
– Juan Ramos, Analytics Engineer, Ebury
Check out how Hevo empowered Ebury to build reliable data products here.
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Google BigQuery
An enterprise-grade data warehouse for analytics, BigQuery is a fully managed and serverless data warehouse. It empowers today’s data analysts and data scientists to analyze data efficiently by creating a logical data warehouse into columnar storage and compiling data from object storage and spreadsheets. Its key features are BigQuery ML, Big Query GIS, BigQuery BI Engine, and connected sheets.
BigQuery is a powerful solution to democratize insights, power business decisions, run analytics, and analyze petabytes scale SQL queries. Bigquery, built on top of Dremel technology, has a serverless architecture. It has decoupled data locality and offers distinct storing and processing clusters.
It differs from node-based cloud data warehousing solutions. It leverages technologies like Borg, colossus, Jupiter, and Dremel to produce optimum performance.
Python
Python is a high-level, object-oriented programming language commonly used to develop websites and software. Python’s applications are also seen in task automation, data analysis, and data visualization. Python is relatively easy to use and learn; hence, it has been adopted by accountants, scientists, data professionals, and others for various tasks, like organizing finances, objectifying 3D models of scientific theories, etc.
Thanks to data analysts and other professionals ‘ quick language adoption, Python has been considered a staple in today’s data science scenario to conduct complex statistical estimates, concoct data visualizations, build machine learning algorithms, and complete other data-related tasks. With the ability to assemble a wide array of complex analytical functions — like data visualizations, like line and bar graphs, pie charts, histograms, and 3D plots — it’s not a surprise why programmers fall in love with the language.
Fivetran
Fivetran, just like Hevo, is a managed data pipeline product. In general, Fivetran standardizes the process to replicate schemas from the source of your choice to a destination like Redshift, BigQuery, and many more. Fivetran uses the ELT approach to load data into a data warehouse, which means loading happens before the transformation process. The product offering helps save crucial person-hours by automating the process of creating SaaS integrations.
SQL
SQL (Structured Query Language), created in the early 1970s, is a ‘standardized programming language.’ SQL is utilized to manage and extract information/data from relational databases. Today, knowing SQL is a prerequisite not only for database administrators but also for software developers. The primary purpose of knowing SQL is to write ‘data integration scripts’ and run analytical queries to transform and use data for business intelligence.
SQL usage includes:
- Modifying database tables and structures — which includes adding and Updating.
- Deleting rows and columns loaded with data.
We can even retrieve subsets of data within the database for numerous use cases for business analytics using SQL. Some commonly used SQL queries and commands include select, add, insert, update, delete, create, alter and truncate.
In general, the SQL commands are of many types, but the most popular ones are as follows: Data manipulation language (DML) and data definition language (DDL). The DML language is employed to collect and manipulate data scripts, while the DDL language is used for defining and revising database structures.
Microsoft Power BI
Microsoft Power BI, a Business Intelligence and Data Visualization tool, is used in analytical use cases to picture data in a more business-friendly manner by converting data sets into live data dashboards and analysis reports. Power BI’s cloud-based services with an easy-to-understand user interface are a godsend for non-technical users to help create reports and dashboards seamlessly.
Power Bi supports hybrid deployment support, which is primarily used in gathering data from different sources to create reports that will power the next business decision you make. The Power BI suite of applications contains the following elements: Power BI Desktop, Power BI Service, Power BI Report Server, Power BI Marketplace, PowerBi Mobile Apps, Power BI Gateway, Power BI Embedded, and Power BI API.
dbt
dbt, a command-line tool, allows data engineers, analysts, and scientists to model and transform data into a warehouse using SQL. As explained by getdbt itself, “dbt is the T in ELT.” So, DBT is responsible for the transformation part of the modern data analytics stack. The software allows a seamless data transformation by simply taking the code and then compiling it to SQL and running parallel to the data warehouse.
Tableau
Researchers describe Tableau as a “highly interactive and intuitive visual-based exploration experience for business users to easily access, prepare and analyze their data without the need for coding.”
In short, Tableau is a data visualization and Business Intelligence tool that is used for business applications like data modeling, creating live dashboards, and assembling data reports to empower business teams to make data-driven decisions.
The product is easy to access and use; hence amongst the favorite in the technically skilled and business teams. Tableau can create all kinds of charts, plots, and graphs. Its distinct graph designs can be positioned strategically for data visualization in a PowerPoint presentation or weekly progress reports. Tableau can work on either type of data set, be it structured or unstructured. Moreover, Tableau is easy to work with and doesn’t require any technical or programming knowledge.
Conclusion
Let’s conclude. This list contains the top 10 Data Engineering Tools, but truth be told, today’s data engineers are spoilt for choice. Nevertheless, for data engineers to build an efficient and robust data infrastructure, these 10 Data Engineering Tools are a godsend.
Because ultimately, the goal is to build a robust and responsive data analytics infrastructure that systematically handles data and can operate for years with minimal tweaking, an ETL/ELT tool is a must-have.
Enter Hevo Data. Hevo is a No-code Data Pipeline, with over 150+ Data integrations, that will ensure a seamless move of all your data from any source to a destination in real-time. Hevo comes with automatic schema management, real-time monitoring & alerts, extensive support, and much more.
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Hope you liked this blog post and have your own take on some of the best Data Engineering Tools. Have a go, and let us know about them in the chat section below.
Yash is a Content Marketing professional with over three years of experience in data-driven marketing campaigns. He has expertise in strategic thinking, integrated marketing, and customer acquisition. Through comprehensive marketing communications and innovative digital strategies, he has driven growth for startups and established brands.
