Scuba Tech Library

What is a Data Platform?

As the sheer volume of recorded human data continues to explode from 4.4 zettabytes in 2013 to 44 zettabytes in 2020, now more than ever, companies are utilizing data platforms to synthesize this overwhelming deluge of information. Whether it’s Amazon congregating user data to predict market conditions, or Oracle pinpointing the weak links in a supply chain, data platforms are how today’s market leaders stay competitive.

Data platforms are software technologies that allow businesses to collect, analyze, and present data. These invaluable tools can be used for a variety of uses, including congregating data across various divisions within a company, sharing metadata to other users, or running recommendation engines.

Different kinds of data platforms

Data platforms fall within specific categorizations, though most incorporate multiple elements. The different kinds of platforms are:

  • Enterprise data platforms are central data repositories that consolidate information across all facets of an organization, including customer service and marketing. This is the foundation of all operational and enhanced data functions.
  • Modern data platforms provide an adaptable architecture of future data analytics. This avoids the common pitfalls of antiquated data lakes by quickly adopting new technologies and optimizing hardware usage.
  • Cloud data platforms are built entirely from cloud computing and data stores. This allows for easier data access and scalability.
  • Data analytics platforms perform analyses across an organization’s various data systems. These turn any and all data collected into actionable business insights.
  • Consumer data platforms create unified consumer databases. These consolidate user data into easily accessible user profiles that can be shared with other systems within an organization.

Benefits of Data Platforms

Data platforms are necessary tools for competitive companies to stay organized, informed, and protected. Some benefits include:

  • Security: Data platforms give organizations control over what data is accessible and who has access to them.
  • Centralization: A good data platform supports all types of sources, such as MySQL, Cassandra, or monteDB.
  • Governance: Data platforms streamline adherence to ever-changing regulatory requirements, thus reducing operational costs and remaining compliant with industry standards.
  • Delivery: Data platforms that allow and enable key functions, such as scheduling and proactive alerts are paramount.
  • Availability: Data platforms should be easily accessible by its user, including high-end point users like data analysts and scientists.

Get real-time data analytics with Scuba

Though there are many different kinds of data platforms, Scuba’s continuous intelligence platform offers unmatched security, data centralization, governance accessibility, delivery, and availability. Scuba offers elevated data management and analytics in real-time, and is designed to be accessible to users across your organization. 

Request a demo or reach out to a Scuba expert today.

Data Science

Data Lakes and Data Warehouses -- Which Is Right For You?

Data lakes and data warehouses are both commonly used for storing data, but there are key differences between the two that make them unique in their own way. Learn which fits your business purposes best and if there is a better solution.

Data Science

What is a Data Ecosystem?

The term “data ecosystem” collectively refers to all the programming languages, algorithms, applications, and the general infrastructure used to collect, analyze and store data.

Data Science

What is Data Modeling

Data modeling is a means of creating a conceptual framework for your data in preparation for storage in a data warehouse. The resulting model is a visual representation of the data which maps out the relationships between data, and the rules.

Data Science

What is Lambda Architecture?

Lambda architecture processes data through a hybrid combination of batch processing and stream processing.

Data Science

What is an ETL Pipeline?

ETL is a method to collect raw data from various sources, clean it up, and translate it so it can be used to inform decision making.

Data Science

What is Data Governance?

Data governance allows organizations to ensure high-quality data through formalized processes for management, monitoring, and control of data assets.

Data Science

What is Hadoop?

Apache Hadoop is one of the most widely used open source frameworks designed to address the problem of storing and processing big data.

Data Science

What is Parquet?

Unlike row-based formats such as CSV, Parquet is a columnar data file storage format.

Data Science

What is Querying?

A query is a question or request for a database written in a code the database can understand, in order to retrieve or modify the correct information.

Data Science

What is an Ad Hoc Query?

An ad hoc query is any kind of question you can ask a data system off the top of your head.

Data Science

What is Metadata Storage?

When building a database, all data requires some description to help identify its uniqueness, which is where metadata comes in.

Data Science

What is an Enterprise Data Warehouse?

An EDW is a database that centralizes data from across the business so it can be analyzed and used in decision making.

Data Science

What is Time-Series Data?

Time-series data analysis serves critical functions in most modern industries, and is a powerful method to glean accurate analysis.

Data Science

What is Data Sovereignty?

Data sovereignty defines the regulations data is subject to. Fortunately, there are actionable steps brands can take to ensure compliance.

Data Science

What is Self-Service Analytics?

Self-service analytics empower non-technical teams to interact with data, perform queries, and glean helpful business insights.

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