Top Data Engineering Tools Utilized Among Tech Companies

The most well-known and in-demand careers in the big data field are in data engineering solutions. Complex data models are created, monitored, and improved by data engineers to assist enterprises in enhancing their business outcomes through the use of data.

Based on data from more than 150 interviews with data engineers, we'll highlight the top 20 data engineering tools used by mid-sized IT organizations in this post. We'll also briefly discuss some patterns we overheard during our discussions to better explore how various data engineering teams are conceiving of their future roles.

Amazon Redshift

A fully managed cloud warehouse created by Amazon is called Redshift. In our conversations with teams, we found that around 60% of them used it. Another industry standard that fuels thousands of enterprises is Amazon's user-friendly cloud warehouse. Anyone can simply build up their data warehouse using the application, and it scales well as your business expands.

Big Query

BigQuery is a fully managed cloud data warehouse, much like Amazon Redshift. Companies that are familiar with the Google Cloud Platform frequently employ it. Engineers and analysts can begin using it when their data sets are small and scale up as their data sets increase. Additionally, it has strong machine learning capabilities that are built in.

Looker

Looker is BI software that aids in data visualization for employees. Looker is well-liked and frequently used by data engineering services. Looker has developed a superb LookML layer in contrast to conventional BI tools. A SQL database's dimensions, aggregates, calculations, and data relationships are all described in this layer's language. spectacles is a recently released tool that enables teams to deploy their LookML layer with assurance. It lets teams to manage their LookML layer. Data engineers can facilitate the usage of company data by non-technical personnel by updating and maintaining this layer.

Tableau

According to our poll, Tableau is the second most often used BI tool. The primary purpose of one of the earliest data visualization solutions is to collect and extract data that is stored in various locations. To use data across many departments, Tableau offers a drag-and-drop interface. The data engineer uses this information to produce dashboards.

Apache Spark

Apache Spark is a free and open-source unified analytics engine for processing vast volumes of data. Apache Spark is a data processing framework that can swiftly perform operations on very large data sets and distribute processes across several machines when used alone or in conjunction with other distributed computing tools. The fields of big data and machine learning, which demand the mobilization of tremendous computing capacity to process vast data warehouses, depend on these two qualities.

Airflow

An open-source workflow management system is called Apache Airflow. In October 2014, Airbnb used it as a way to handle the growingly complicated operations of the business. Airbnb was able to automatically author, plan, and track its processes using Airflow thanks to the creation of the Airflow user interface. About 25% of the data teams we spoke with used it, making it the most popular workflow management option.

Apache Hive

Apache In order to provide data query and analysis, Hive is a data warehouse software project developed on top of Apache Hadoop. Hive provides a SQL-like interface for querying data held in multiple Hadoop-integrated databases and storage systems. Data summarization, analysis, and querying are the three key functions for which Hive is used. HiveQL is the only query language supported by Hive. For use on Hadoop, this language converts SQL-like queries into MapReduce jobs.

Segment

Segment makes it easy to gather and utilize user data for your digital assets. You may gather, modify, transfer, and archive your customer data using Segment. Teams can work more efficiently since the technology makes it easier to collect data and connect it to new technologies while also saving time.

Snowflake

 Today's enterprises need the performance, scale, elasticity, and concurrency that Snowflake's distinctive shared data architecture offers. We found that a lot of the teams we spoke with were curious about Snowflake and its ability to store and process data, so we anticipate that more teams will make the switch in the upcoming years. Snowflake is the perfect platform for data warehousing, data lakes, data engineering, data science, and creating data applications since its data workloads scale independently of one another.

dbt

Data engineering solutions and analysts can utilize DBT, a command-line tool, to use SQL to change data in their warehouse. DBT does not provide extraction or load operations because it is the transformation layer of the stack. Companies may write transforms quickly and more effectively because to this technology. The product was created by Fishtown Analytics, and data engineers are gushing about it.

Redash

Redash is made to make it possible for everyone, regardless of technical proficiency, to harness the power of data, both large and little. Redash is used by SQL users to browse, query, view, and share data from any source. Everyone in their organization can use the data as a result of their efforts with little to no learning curve.

Fivetran

A robust ETL tool is Fivetran. The effective collecting of client data from relevant servers, websites, and applications is made possible by Fivetran. To use other tools for analytics, marketing, and warehousing, the obtained data is first relocated from its initial location to the data warehouse.

Apache Kafka

Real-time streaming data pipelines and applications that can respond to those data streams are mostly created using Kafka. Streaming data is information that is constantly produced by thousands of data sources, most of which transmit records in at the same time. In order to create networks between people, Kafka was initially developed at LinkedIn, where it helped analyze the connections between its millions of professional users.

Power BI

Microsoft offers a service for business analytics called Power BI. It seeks to offer business intelligence capabilities and interactive visualizations with a user interface that is straightforward enough for users to build their own reports and dashboards. Organizations can use the Power BI data models in a variety of ways, including to investigate "what if" scenarios in the data and tell stories using charts and data visualizations.

What are data teams eager to use the most?

Nearly all data engineering services concur that DBT is the most fascinating tool they want to learn or use. A community for analytics engineering has been brilliantly developed by the Fishtown Analytics team. Data engineers can use the tool, which is a command-line, to change their warehouse using SQL. Due of its streamlining of procedures for data engineers, it just raised a sizeable funding round.

Second, a lot of the people we spoke with were either planning to test Snowflake or were already going in that direction. The tool's usefulness is highly regarded by its present users, who would suggest it to anyone searching for a data warehouse.

Addressing the communication issue

Teams tend to start having issues identifying and evaluating data once the procedures of collection and cleaning have been completed. These issues don't develop because people lack the information or abilities to arrive at the right solution. We refer to this as the data debt issue. Instead, it's a problem brought on by non-collaborative, segregated data. For teams seeking to agree on what to monitor and how to define critical metrics, this creates a communication issue.

Use a new way of working and communicating to solve the challenges with data communication that exist now

It's simple to forget that the solution to communication issues in today's data-driven environment extends beyond simply improving communication.

It's simple to point the finger at and berate others for our communication breakdowns. It's tempting to criticize others for their "failures to understand" or poor interpersonal skills. There will always be those who don't appear to be able to understand us, but at the end of the day, we're all just humans trying to make sense of our common environment.

Better solutions exist. Other approaches to addressing the issue of data-driven communication include creating a system where everyone works to understand one another better, developing a platform that enables everyone in a team, organization, or even across the globe to see and understand one another's work, utilizing a common platform so that everyone can see what one another is working on, and developing a productized learning experience for anyone who wants to learn more about data communication.

Use the same information to get everyone on the same page

You must make sure that your team is using the same data and information in order to fix this issue. You're going to have various individuals with various duties on any team. Some are in charge of gathering the data, others are in charge of entering it, and still others are in charge of evaluating it and communicating the conclusions they've drawn from it. Everyone needs a system that operates consistently and reliably throughout all of your company's departments, even though each of these positions necessitates a somewhat different viewpoint on the information.