This is a guest article from our friends at Dataquest. Dataquest is a data science learning platform with a quickly growing community of learners.
Data is critical for the growth of a business. Nowadays, teams must collaborate and work on the data ecosystem at a faster rate. This is where a data workbench or an end-to-end data science and machine learning platform such as Dataiku can be instrumental.
What Is a Data Workbench?
A company’s data and analytics team consists of data scientists, engineers, and project managers. Even though it’s a mix of skill sets, the end goal is to work collaboratively and make sense of the organization’s data.
A cloud-native data workbench takes care of all aspects of data extraction, from processing to model deployments and visualization. The components of a data workbench can be divided as follows:
- Data Plane: This is where the data engineers work. They build or utilize data connectors, maintain metadata, schedule, and monitor data pipelines.
- Analytics Plane: The analytics plane is where data scientists analyze data on the data plane to build and deploy models and visualizations.
- Business Intelligence Plane: In this plane, the project managers can use models and visualizations to derive insights from generated KPIs.
Let’s break down each plane.
Data Plane
The data plane is where data is brought in, transformed, and stored in data lakes and warehouses. Data engineers work on the warehouse design based on extracted data. Essentially this is where ETL (extract, transform, and load) capabilities in a workbench can come in handy, rather than building the tool from the ground up.
Additionally, a hybrid approach can be used where engineers incorporate SQL queries as part of their workflows for more sophisticated transformations. After building ETL workflows, they are scheduled to run and monitored further for tracking. As a result of these workflows, warehouses are created and handed over to analysts and data scientists.
Analytics Plane
The analysts and data scientists consume the data from the data warehouses, identifying key performance indicators (KPIs) using SQL and analytical engines to process stored data. Interestingly, analysts and BI engineers can build applications (also known as apps) by taking the KPI tables and building visualizations. These apps make the visualizations and dashboards easily shareable with corresponding business teams. Data scientists also build machine learning models by using inference and deploying it in APIs for the other web services to use.
Business Intelligence Plane
The business team uses the apps that the analysts and BI engineers build. They share it with stakeholders to get a holistic perspective. A holistic perspective helps to derive proper insights and make business decisions accordingly.
Challenges Solved by the Data Workbench
The three planes mentioned above work together like clockwork in collaborating and facilitating in all aspects of the company's data sector from data onboarding and creating models and apps to deriving business insights. In addition, the data workbench solves a multitude of challenges for a business including:
- Collaboration - Workbenches come with batteries included, meaning that they come with everything needed. For example, Dataiku comes with Git-based source control built into its platform. With source control, it’s safer to collaborate and easier to develop features into existing models and pipelines.
- Environments - With collaboration comes diversity. The good part about data workbenches is the support for multiple coding environments.
- Self Serve - Upon providing app access to the required teams, it’s up to them what they want to explore. The same strategy applies to data analysts; they can choose whichever tables they wish to query from. This drastically reduces development time and dependency checks between the team.
- Security - Data is securely hosted in the cloud, with BI dashboards and visualizations embedded in the workbench; both the data and IP are secure on the cloud. Even better, the workbench can be restricted only to be accessed from a virtual private network providing extra security.
- Scalability - Building a scalable solution is important. When an organization grows, the analytical processing part of the workbench can be scaled up when there’s peak traffic and scaled down when there’s meager traffic.
- SQL Support - SQL is an essential skill across different roles, so extended support to query datasets using SQL aids in faster adoption of the workbench across various organization teams.
Does Your Organization Need a Data Workbench?
And now, the moment of truth. Does your organization need a data workbench? The short answer — yes, absolutely. Without a data workbench in place, collaboration between teams could turn into chaos. There’s always a paradox in the choice between building a data workbench through combining tools versus buying an end-to-end platform. Building a near-perfect data workbench is possible, but it requires a lot of time and effort. It also requires a dedicated team to monitor and upgrade the solution as needed. When productivity is considered the need of the hour, a data workbench platform is a must-have solution for every organization.