Guide: DataOps

All You Need to Know About DataOps Tools

Now that businesses are dealing with big data on an everyday basis to generate useful insights, we need more efficient software and data development lifecycles. The era of big data calls for some robust data operations (DataOps) tools that can automate processes and decrease the cycle time of the data analytics for huge datasets. Moreover, these DataOps tools foster collaboration that’s critical to scale development teams and boost their capacity.

In this guide, we’re going to talk about:

This article is a chapter in our DataOps guide.

Why Do You Need DataOps Tools?

DataOps tools serve as command centers for DataOps. These platforms orchestrate individuals, processes, and technology to provide a reliable data pipeline to their consumers. 

DataOps tools bring together several kinds of data management software into a discrete, integrated environment. You can use these platforms to leverage any analytical tool –from data collection to data reporting via a single integrated platform. The platform unifies all the development and operations in data workflows.

DataOps platforms are used to:

  • Provide the flexibility to support a plethora of existing and new tools
  • Control the entire workflow and related processes
  • Ensure data-driven decisions are being made
  • Reduce cycle times significantly
  • Empower users with a single point of access to manage the data
  • Derive on-demand insights for successful business decisions

The Five Types of DataOps Tools

DataOps tools available on the market today can be broadly classified into five categories.

1. All-in-One Tools

All-in-one tools include most of the components essential to build, test, monitor, and deploy data pipelines in a single integrated GUI-based environment. These tools are ideal for companies that want to standardize on a single integrated platform for building, running, and monitoring data pipelines.

All-in-one tools streamline management, fast-track adoption, and decrease costs. However, apart from their relative immaturity, a major drawback of these tools is that they may not have everything a consumer requires or wants.

2. Orchestration Tools

These tools focus solely on DataOps processes instead of trying to be all things to all customers. They wrap around a company’s current data management tools with a DataOps overlay that offers continuous testing and monitoring to ensure high quality and fast cycle times.

Orchestration tools are great for businesses that have invested large amounts of money and time into data management tools and do not want to introduce another product.

The orchestration software applies continuous integration, continuous delivery, and continuous testing and monitoring to existing components, helping businesses enhance quality and cycle times without altering core development tools.

The downside with orchestration solutions is that they work within the constraints of the native tools they are orchestrating.

3. Component-Specific Tools

You need several different components to create, execute, and manage a data pipeline, and these tools are individual products for every component. 

For example, a company could purchase separate tools for continuous integration, continuous delivery, configuration management, performance management, and so on.

4. Case-Specific Tools

These tools are designed to support a specific domain of DataOps, such as data science (AiOps), data warehousing (DW automation), cloud migration (CloudOps), and so on.

For instance, you can use a DW automation tool to automate updates to data marts and data warehouses, or an AiOps tool to streamline the data science workflow with audit trails, advanced experiments, continuous integration, and deployment.

5. Open Source

You’ll find plenty of open source DataOps tools on the market, many of which are quite popular, particularly in the DevOps world.

For instance, GitHub is the leading source code repository and Jenkins is a prominent CI/CD tool. Also, Apache Airflow is one of the most prevalent data orchestration tools available today.

DataOps Tools Examples

As DataOps evolves, many companies are developing programs and tools to support this approach to data analytics and processing. The tool you choose should depend on your objectives, the data volume you’re dealing with, and other applications or tools you need to integrate.

Let’s take a look at the six most popular DataOps tools examples

1. DataKitchen

DataKitchen DataOps tool is an example of a tool for automating and coordinating individuals, environments, and tools in data analytics of the entire organization. It handles it all –from testing to orchestration, to development, and deployment.

You can easily meta-orchestrate data processes, tools, and teams irrespective of location or environment (for instance, on-premises, cloud, multi-cloud, or hybrid).

With DataKitchen, your business can reduce errors and deploy new features much faster. It allows you to spin up repetitive work environments quickly so teams can experiment without breaking production cycles.

2. TENGU

TENGU is a DataOps orchestration tool designed specifically for data-driven businesses. It empowers them to increase the efficiency of data and boost their business by making this data most beneficial and available at the right moment.

It was created in 2016 as a solution to automate non-value generating tasks and more quickly set up data architectures, so there would be more time to dedicate to gaining actionable insights.

By using TENGU, business, analytics, and data teams require fewer meetings and service tickets to gather data. They can start right away with the data relevant to furthering the company.

3. MLflow

MLflow stands for Machine Learning flow. It is an open-source cloud-based solution that you can use to run DataOps. This platform was designed to solve the issue of multiple data analytics tools, which made it difficult to move through a DataOps cycle with agility and continuity.

You can use MLflow to manage the entire ML lifecycle, which includes experimentation, reproducibility, deployment, and a central model registry.

MLflow currently offers four components: MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry. It can work on any language or with any coding. It can be used by one user or an entire organization with several users.

4. HighByte Intelligence Hub

HighByte Intelligence Hub is the first DataOps tool purpose-built for industrial environments. It provides industrial businesses with an off-the-shelf software solution to fast-track and scale the usage of operational data throughout the extended enterprise by contextualizing, standardizing, and securing valuable information.

The platform runs at the Edge, scales from embedded to server-grade computing platforms, connects devices and applications through an extensive range of open standards and native connections, processes streaming data via standard models, and provides contextualized and correlated information to the applications that need it.

5. StreamSets

StreamSets DataOps tool allows your whole team, from highly skilled data engineers to visual ETL developers, to perform powerful data engineering tasks. It simplifies how you build pipelines quickly with intent-driven design and easily extendible features to meet intricate enterprise needs.

You can easily build smart data pipelines in minutes and deploy them across hybrid and multi-cloud platforms from a single log-in. It is a cloud-native platform designed to control data drift, i.e. the problem of variations in data, data sources, data infrastructure, and data processing.

6. K2View

K2View is an all-in-one platform that brings all the DataOps tools that an organization needs under one roof so you don’t have to think about integrating multiple tools. It provides you with a single dashboard to monitor and digest all the information you need, whenever you need it. The various integrations also ensure that anyone in your organization who needs access to the data gets the interpolated and real-time information they need.

Moreover, it offers full, exhaustive data on any product, customer, location or area, demographic, and more information that is up-to-date and relevant, instead of lagging or growing old. 

The continuous delivery of data, an adaptable and flexible framework that reacts to the incoming data, and security support are some of the benefits the customers really like about this product.

Build vs Buy in DataOps Tools

Before making decisions about building or buying a DataOps tool, you should test the market to consider a design thinking approach before building. This will allow you to conserve resources and energy to be dedicated to building only where such development is essential.

If the platform is not linked to your business’s core value proposition, or will not affect revenue growth directly, then there is no need to allocate time and resources on building a personalized DataOps tool in-house. It is only astute to do so if your company is big enough to divide the cost of the development and maintenance across a large number of customers.

However, if you’re looking for a tool to support revenue generation directly, then developing a DataOps tool in-house might be the best route to take. That’s because owning the technology is beneficial if the tool is inherently connected to your business’s USP.

Collaborating with 3rd party vendors and professionals can be useful as they bring an outsider’s perspective to the mix. This can help you highlight issues you may not be aware of. Moreover, it can bring experience-based approaches and processes to your company that you could otherwise have lacked.

Irrespective of whether you choose to build or buy, your final product must stem from an understanding of the connected nature of data engineering, data management, data quality, and data security. Therefore, DataOps can be seen as an approach that uses continuing automated orchestration, testing, and reporting to communicate within an organization.

DataOps Security with Satori

Satori is the first DataSecOps platform, allowing organizations to integrate security as a first-class citizen in their data operations. Some of Satori’s capabilities are continuous discovery of sensitive data, mapping of the sensitive data within the data assets you have, enabling auditing and monitoring of your data access, and streamlining access to sensitive data with access workflows.

To learn more about Satori, visit our product page.

Conclusion

There are many DataOps tools available in the market today that help foster the collaboration that’s critical to scale development teams. These tools also help facilitate data pipeline orchestration, testing and production quality, deployment automation, and data science model deployment/sandbox management.

As DataOps is an ever-evolving framework with a comparatively immature marketplace, there are a plethora of new tools being introduced. Older data tools and tech are also being revamped with newer technology. With DataOps tools gradually entering the world of smarter products, companies can utilize fully managed platforms to create autonomous data pipelines that not only fuel analytics but also machine learning applications.

The bottom line is that it’s indispensable for businesses to leverage DataOps tools so that their teams can adopt and collaborate expediently while working with huge datasets on an everyday basis.

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