What is a data platform? Benefits and types

Since data tends to scale horizontally, it can easily get out of hand. Many companies end up adding countless extra tools and systems to their setup in response, but ultimately just overcomplicate their ecosystem. 

That’s where data platforms come in. These frameworks unify your entire data architecture into a singular system, centralizing your organization’s content and making it easier to interact with, understand, and analyze. 

In this guide, we explain why data platforms are so vital and how you can get the most from them, so that instead of a complex web, you have a streamlined architecture that’s ready for success.

What is a data platform?

A data platform is an integrated framework that brings together different tools so you can easily collect, store, clean, transform, analyze, and govern your data.

Since data platforms make high-quality data more readily available, they help your teams make faster, more efficient data-driven decisions. More broadly, they also break down data silos, enable self-service analytics, power AI applications, and surface insights from across your entire company’s data.

Note: You may see some companies refer to Big Data platforms. “Big Data” is a term that originated in the 1990s, when many companies’ data generation capabilities accelerated rapidly. “Big Data platform” used to refer to any platform that could manage organizational expectations around this boom. Today, though, most modern data platforms can handle these requirements with ease.

Data platform vs. data warehouse vs. data lake

Data warehouses store large amounts of data from multiple sources in a central location for data analysis, BI, and reporting. To improve accessibility and streamline the analysis process, many companies choose to clean and standardize data before loading it into a warehouse.

While warehouses work well with structured data, they struggle to provide the flexibility required to support semi-structured and unstructured content. Data lakes solve this problem by allowing you to store large volumes of raw data at a low cost through a schema-on-read system that only structures data when it’s used in a query. 

Instead of having to interact with lakes and warehouses separately, data platforms combine the capabilities of systems (and any other data architecture like ELT/ETL pipelines) into one view for easy access to querying, analysis, and reporting. 

How do data platforms work?

Here’s a high-level overview of how data moves through a data platform framework:

1. Collection: The first step is extracting data from a variety of sources, like IoT sensors, social media interactions, SaaS apps, databases, and your company website. 
2. Storage: Data is then stored in a repository like a data warehouse, lake, or lakehouse.
3. Processing: The data is filtered, cleaned, standardized, transformed, and aggregated so that it’s in a useful format. Data integration platforms like Fivetran can automate collection, storage, and processing while providing real-time monitoring of your content.
4. Analytics: The data is then analyzed by tools and methods such as predictive analytics, data visualization, and ML algorithms. 
5. Governance: To ensure data is correct and complete, governance policies are then applied. This includes cataloguing, lineage tracing, and quality management.
6. Management: Continually analyzing the performance of a data platform helps uncover ways to optimize its output. Create backups and archive your data when required to keep your system healthy.

From source to ongoing management, these data platform features help you turn raw information into actionable business insights.

What are the 6 layers of a cloud-based data platform?

Modern data platforms are made up of six fundamental layers. Here’s how each supports the flow of information through your company. 

1. Data sources layer

Data sources are the point of origin from which all information flows into your business. Different sources may provide distinct file types, content structures, and information, meaning you’ll need to build tools that integrate data effectively.

2. Data ingestion layer

The ingestion layer streamlines extraction, allowing you to faithfully replicate data from identified sources. You can do this in bulk or incremental batches, depending on your specific needs. 

3. Data processing layer

Data processing transforms raw information into a more structured format. This can include cleansing, formatting, normalization, enriching, or aggregating data to improve its quality and accuracy.

4. Data storage layer

Typically, data platforms store content in a data warehouse, data lake, or data lakehouse that combines features of both.

5. Data analytics layer

Analytics tools connect to your storage layer, allowing you to query and run analytical models to draw insights, spot patterns, and inspect data to find quantitative details that help you make better decisions. 

6. Data visualization layer

Visualization tools transform data insights into visual media that make it easier for non-technical users to understand. Common examples include reports and graphs, but you’ll also find dashboards that update in real time. 

Understanding modern data platform architecture

Many companies use the terms “data architecture” and “data platform” interchangeably, but there are important distinctions between them. 

A data platform is a system that reads, transfers, analyzes, and validates data for end users. Architecture, on the other hand, is your organizing principle for how to ingest, store, and deliver data. Key considerations include:

• Scalability: Data architecture needs to regulate the flow of information inside an organization, adapting to new sources and evolving as source schemas change.
• Automation: To efficiently organize and deliver data, architecture should automate ingestion and distribution as much as possible, whether that’s using a fully managed ELT pipeline like Fivetran or employing AI to spot data quality issues.
• Governance and security: Architecture outlines how your business ensures that all its data is up to regulatory standards, including the tools you deploy to keep content safe and secure.

Advantages of data platforms

Unified cloud data platforms aim to give you the most value possible from your data, whether that means helping people tackle new challenges or understanding consumer behavior. Here are the major benefits of using them:

• Enhanced data collaboration: Data platforms centralize information and break down data silos, helping teams collaborate by using the same data.
• Faster data ingestion and analysis: By reducing the number of tools you have to interact with before you can access your information, data platforms boost agility across your systems.
• Robust data governance: Improving visibility over your entire ecosystem lets you apply governance policies consistently and meet regulations.
• Cost-effective: Through improvements to data observability, you get more from your data and reap the benefits of data-driven decision-making at a lower cost.

Data platform example use cases

By centralizing data and improving its utility, any business can benefit from an enterprise data platform. Here are a few example use cases.

Finance

Meet strict regulations by applying consistent policies across your data platforms. Identify fraud rapidly with faster insights and improve operational efficiency with real-time data.

Healthcare

Data platforms help medical businesses protect patient records and comply with HIPAA.

Monitor who has access to specific records, apply controls, and enforce data loss prevention systems. 

Retail

Optimize inventory management with live insights, predict demand with analytics, and deliver better customer service with improved user behavior tracking. You can also connect sales, supply chain, and customer data to create a more personalized and responsive shopping experience.

How Fivetran powers your data platform

While data platforms centralize your informational tools, they still require timely, accurate data to run effectively. Instead of building complex ingestion pipelines and setting your engineering team on heavy-duty maintenance, Fivetran helps you improve ingestion and enhance governance through automated, fully managed ELT pipelines. 

With the push of a button, you can extract data from sources, load it into your system, and get everything ready for analytics. With over 700 pre-built connectors, you can draw from SaaS apps, databases, and file systems with no need to write any code. It’s an ideal solution for any business in need of consistent, high-quality data, automated schema drift handling, and fully managed data integration.

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