Data Platforms: What You Need to Know

What it means, why it matters, and best practices. This article provides definitions and insights into data platforms.

Data Platform

Using a data platform, you can get a more robust view of your data and manage it easily and effectively. There are many different types of data platform services, so you should aim to find a platform that meets your specific needs. If you’re a data engineer, for example, you should seek out a platform for data engineers. Similarly, if you’re looking for a cloud-based solution as opposed to an offline platform, you might narrow your search to online platforms only. This can make it easier for you to find the most appropriate cloud data platform for your unique business needs.

One of the critical aspects of working with a data platform is that you can compile your data in a single location. Rather than having to pull data from disparate systems, you can easily access what you need from one platform. You can more easily keep track of your data, quickly spot issues as they occur, and work to resolve those issues in a timely fashion. Data platforms equip users with the tools they need to monitor their data pipelines and ensure that data gets where it needs to be and is properly utilized to the greatest extent possible.

Acceldata is a multidimensional data observability platform that offers complete visibility into data performance, allowing users to make the necessary changes to improve the quality of their data. Acceldata predicts operational issues before they spiral out of control, correlates events, and helps organizations avoid poor business decisions due to  unreliable data. The platform offers three products (Pulse, Torch, and Flow), each of which play a critical role in helping users sort and analyze data for maximum efficiency. With Acceldata you can get more out of your data system and drive greater revenue, ultimately promoting business growth across the board.

Data Platform Architecture

Data platform architecture outlines the steps to capture and deliver data quality. Data observability architecture is a type of architecture that allows for holistic visibility into the data management process, helping users to better understand where their data is coming from, where it’s going, and how it can be used to improve business operations. Big data platform architecture is likewise useful for understanding big data, or data sets that are so large that they cannot be appropriately stored and processed by standard data tools. Different architectures are most useful for different types of data, and so it’s important to consider the type of data you’re working with and what you’re hoping to do with it.

You can view a data observability platform architecture diagram to learn more about data observability architecture. This can give you a better sense of how different aspects of observability impact one another and what’s all involved in monitoring data quality. By understanding each of the data platform layers, you can better optimize your monitoring process. You might also study a modern data platform architecture to learn more about how to modernize your data system for better results.

Data Platform Tools

Data platform tools enable users to monitor their data. But what is a data platform? As discussed previously, data platforms extract, analyze, and store data, making it easy for businesses to manage their data in a centralized location. Just as there are different data platform types, there are different types of data platform tools. For instance, if you work for an enterprise company with large volumes of data, you might look specifically for an enterprise data platform. This may be better suited to your needs than, say, a platform designed for small businesses responsible for smaller volumes of data.

There are a wide variety of data platform companies, so when looking for a data observability platform, it can be helpful to outline your specific needs in order to narrow down your search. If you use Apache Hadoop, for example, then data platforms Hadoop tools may be most useful for your specific needs. It’s important to choose tools that are as closely aligned to your software or framework as possible. This will not only allow you to get full visibility into your data workflows but ensure continuity and prevent errors. Defining your specific needs and requirements ahead of time can help you choose the right tools for your data.

Data Platform Examples

Viewing data platform examples can give you a better idea of what to expect when using your own platform to manage data. This can also help you understand data platform vs. database because, while the two terms are often used interchangeably, they refer to different things. There are many different types of data platform examples, so no matter what you’re looking for, you should be able to find an example that’s helpful for your purposes. If you work with customer data, you should view customer data platform examples; if you want to learn more about the best platforms overall, you should search for the top data platforms, and so on.

Different data platform companies offer different capabilities and integrations. For example, Acceldata offers support for Hadoop users, allowing them to cut costs by 50% or more. Users can remain on premises with the Hadoop release they have now without the need for continued Cloudera support. Boasting the best Hadoop SLAs in the industry, Acceldata enables users to optimize their ecosystems and get more out of their software, even as Cloudera sunsets support for HDP/CDH. Those that are looking for data platforms Hadoop solutions can find value in Acceldata’s multidimensional data observability platform.

Data Platform Gartner

Data platform Gartner tools are extremely useful for comparing platforms. Browsing the Gartner Market Guide for Customer Data Platforms can give you a better idea of what kinds of platforms exist today. Viewing customer data platform examples can also be useful for seeing how these platforms work in action and the various ways in which they benefit organizations. Different customer data platform companies offer different types of tools and solutions, so it’s important to thoroughly consider your options to find the platform that’s best for your business.

The Adobe customer data platform is one of the most widely-used CDPs on the market and offers users a real-time look into their data so that they can collect and unify it for activation. Other top customer data platforms provide similar functionality and are critical for understanding data on a deeper level. Acceldata is one of the only platforms that offers multidimensional observability, allowing users to get a comprehensive view of their data from all angles so that they can drive better decision-making. The platform gives users the tools they need to optimize their data systems and ensure quality across all layers. This can result in higher data engineering productivity, quicker application development, and fewer incidents.

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