What is a data analytics platform?

A data analytics platform is a unified solution that combines technologies to meet enterprise needs end-to-end throughout the analytics lifecycle. Traditionally, analytic environments were built using multiple discrete components that needed to be acquired, integrated and managed. By contrast, a data analytics platform provides all these capabilities in a single integrated offering.

This article focuses on the modern architecture behind data analytics platforms. For the platform category and evaluation criteria, see what is an analytics platform?, and for deployment options, see what is cloud analytics?

What are the benefits of using a data analytics platform?

Data analytics platforms are attractive to businesses because they:

• Connect directly to legacy sources and business applications
• Help reduce time spent on data preparation
• Allow business users self-service access to data
• Integrate data across the organization, speeding time to insight
• Increase the return on existing tech stack investments

Why switch to a data analytics platform?

The traditional approach to building an analytics infrastructure is no longer working for many organizations. In the past, organizations relied on expensive data warehouse platforms to support business intelligence (BI) and decision support systems (DSS). Organizations typically relied on data integration solutions to extract, cleanse and aggregate data using complex ETL pipelines before loading data into the warehouse. Not only was this expensive, but it was also time-consuming. Each time business users required access to new data sets, data engineering assistance was required to build and validate new pipelines. This process could take weeks or even months as busy data engineering teams worked through a backlog of requests.

Fortunately, a variety of new technologies such as optimized Parquet file formats, columnar data stores and in-memory query engines now make it possible to analyze data sets without the need for extensive data manipulation and discrete tools.

The technologies behind a modern data analytics platform

• Columnar storage: Data is stored by column rather than by row, so queries read only the fields they need. This speeds up analytical workloads that scan many records but few attributes.
• Parquet and open file formats: Optimized, compressed formats such as Parquet store large data sets efficiently and can be read by many different engines and tools.
• In-memory query engines: Holding working data in memory allows complex joins and aggregations to return quickly, even across large, detailed data sets.
• Lakehouse architecture: A lakehouse combines the low-cost, flexible storage of a data lake with the management and query performance associated with a warehouse.
• Direct connectivity: Data connectors bring data from source applications with less transformation up front, preserving original structures and detail.
• Semantic layer: A semantic layer translates technical data into business terms so different tools and users work from consistent definitions.

Data analytics platform vs. traditional data warehouse

A traditional warehouse requires data to be modeled and aggregated before it is loaded, so the questions it can answer are largely decided in advance. A modern data analytics platform keeps data closer to its original form and detail, allowing users to ask new questions without waiting for new pipelines. It also brings integration, storage, analysis and data science into one environment instead of spreading them across separate products. The result is closer to unified data analytics, where one platform serves many workloads.

Who uses a data analytics platform?

• Business analysts build reports and dashboards and explore data to answer operational questions.
• Data scientists use the same data for modeling, forecasting and machine learning, often through notebooks.
• Business users and decision-makers access self-service analytics and data apps to monitor performance and act.
• Data engineers spend less time on pipeline maintenance and more on governance and higher-value work.
• AI agents increasingly query governed data directly, as described in what is an AI data platform?

Challenges to plan for

• Data quality: Faster access to source data makes inconsistencies more visible, so quality and governance processes still matter.
• Change management: Teams used to warehouse-centric workflows need time to adopt new ways of working.
• Integration with existing tools: The platform should work alongside BI and data science tools already in use.
• Security: Broader self-service access requires well-defined roles and permissions.

Incorta unified data analytics platform

The Incorta unified data analytics platform is a new breed of analytics platform that leverages these advances to address the challenges of traditional data warehouses and analytic environments. Incorta is a "full-stack" solution that combines data acquisition, data processing, data curation, analysis and data science tools, accessible through a single web interface.

By delivering exceptional query performance, modern data analytic platforms enable business analysts, decision-makers and end users to work together and efficiently analyze large and complex data sets in real time. Unlike traditional BI solutions that have rigid data modeling requirements, are costly and have long implementation times, with Incorta organizations benefit from a dramatic reduction in cost, complexity and data engineering workload, enabling them to improve productivity and agility significantly. Learn more about the Incorta platform.