What is a Data Platform?

Do you really want a competitive edge for your business in today’s digital era? Are you effectively managing and leveraging enterprise data? It is a key differentiator for companies. No matter whether you’re a startup or a global enterprise, a robust data platform can empower you to make informed decisions, optimize operations, and gain that competitive edge. It updates the whole data infrastructure, thus giving a single architecture that allows organizations to carry out their daily data activity such as gathering, storing, processing, and analyzing data effectively.

In this blog, let us explore what data platforms are, their layered architecture, types, benefits, popular tools, and how to select the right platform for your business needs.

Let’s dive right in!

Defining Data Platform

A Data platform is a unified technology architecture that unifies the management of data throughout its full life cycle, from data consumption to deep analysis. It supports a unified environment that enables firms to process structured, semi-structured, and unstructured data from various origins in a repeatable, extensible, and secure way.

Current platforms extend data warehouses by having enhanced features, including real-time data processing, machine learning, data governance, and self-service analytics. They help organizations shatter data silos, enhance the quality of data, and achieve actionable insights more quickly.

Key characteristics include:

• Unified data storage
• Scalability for large datasets
• Support for batch and streaming data processing
• Advanced data transformation and modeling
• Robust security and compliance features
• Integration with various BI and machine learning tools

Layers in a Data Platform

A data platform typically consists of multiple layers, each serving a distinct function in the lifecycle of the data where it is turned into insights from its raw form. Let us begin by talking about the fundamental layers in summary.

1. Storage and Processing Layer

The data storage and processing layer is one of the most basic elements of any data platform. It handles raw data storage and initial data processing functions. The storage layer is elastic and provides virtually infinite storage with instant retrieval along with archival functionalities.  

Data Storage

Data can be stored in the following different storage architectures:

• Data Lakes: Choose this architecture when you have enormous data that holds raw, unstructured, and structured data formats. (e.g., Amazon S3, Google Cloud Storage, Azure Data Lake Storage)
• Data Warehouses: Choose this when you have structured data with analytical needs. (e.g., Amazon Redshift, Google BigQuery, Snowflake)
• Databases: Relational and NoSQL databases for transactional data. (e.g., PostgreSQL, MongoDB, DynamoDB)

Data Processing

Processing engines convert raw data into usable forms.

• Batch Processing: Apache Hadoop, Apache Spark, AWS EMR
• Stream Processing: Apache Kafka, Apache Flink, AWS Kinesis

2. Ingestion Layer

Data Ingestion refers to the act of gathering data from different internal and external sources and loading it into the platform for processing.

Types of Data Ingestion:

• Batch Ingestion: Scheduled data uploads (e.g., AWS Glue, Apache NiFi, Talend)
• Real-Time Streaming: Ongoing data stream from event-driven systems (e.g., Apache Kafka, AWS Kinesis, Google Cloud Pub/Sub)
• API-Based Ingestion: Data gathered through APIs (e.g., Airbyte, Fivetran)

Contemporary data platforms tend to blend batch and streaming ingestion to enable hybrid use cases.

3. Transformation and Modeling Layer

After data is ingested, it needs to be cleaned, transformed, and modeled into a structured form for analysis.

Data Transformation Tools:

• ETL Pipelines: AWS Glue, Apache Spark, Talend
• Data Modeling: DBT (Data Build Tool), LookML, SQL-based transformations

Some of the common transformation tasks are data cleansing, aggregation, joining datasets, and business rules application.

4. Business Intelligence (BI) and Analytics Layer

The BI and Analytics layer enables business users and analysts to gain insights from data transformed through reporting and visualization.

BI and Visualization tools:

• Self-Service BI: Tableau, Power BI, Looker
• Embedded Analytics: Google Data Studio, Metabase
• Ad Hoc Analysis: Jupyter Notebooks, Apache Superset

This layer delivers dashboards, visualizations, and reports that assist stakeholders in making data-driven decisions.

5. Observability Layer

Data Observability ensures that the overall data pipeline’s health, accuracy, and performance are maintained.

The most important observability factors are:

• Data Quality Tests
• Data Lineage Tracking
• Performance Monitoring
• Alerting and Incident Management

Most Popular Tools:

• Monte Carlo
• Great Expectations
• Datafold

Other Important Data Platform Layers

Modern data platforms often include additional layers to enhance functionality and data governance.

1. Data Governance

Data governance defines policies and procedures for data access, security, and compliance.

• Tools: Collibra, Alation, Informatica

2. Data Cataloging and Metadata Management

Data catalogs improve data discoverability by organizing metadata and data lineage.

• Tools: Apache Atlas, Amundsen, AWS Glue Data Catalog

3. Data Discovery

Data discovery enables business users to find relevant data without relying on technical teams.

• Tools: DataHub, Alation, Amundsen

4. Machine Learning and AI

Advanced platforms integrate machine learning capabilities for predictive analytics and AI applications.

• Tools: TensorFlow, AWS SageMaker, Databricks

Types of Data Platforms

Data platforms can be classified based on their architecture, purpose, and deployment model.

1. Enterprise Data Platform (EDP)

Enterprise Data Platforms (EDPs) are designed to support large organizations with complex data needs. They integrate data from various sources, including CRM, ERP, and transactional databases, ensuring smooth data flow across the enterprise.

Key Features:

• Strong data governance and security
• Support for both structured and unstructured data
• Scalability to handle large volumes of data

Examples: SAP Data Intelligence, Oracle Data Platform, IBM Cloud Pak for Data

2. Big Data Platform (BDP)

Big Data Platforms (BDPs) are optimized for handling massive datasets with distributed computing. They process large-scale data using parallel processing techniques.

Key Features:

• Distributed computing architecture
• Support for batch and real-time data processing
• High-speed analytics

Examples: Cloudera Data Platform, AWS EMR, Apache Hadoop

3. Cloud Data Platform (CDP)

Cloud Data Platforms (CDPs) are cloud-native, offering fully managed services with high scalability and minimal infrastructure management.

Key Features:

• On-demand scalability
• Cost-efficient pay-as-you-go model
• Seamless integration with cloud services.

Examples: Snowflake, Google BigQuery, Amazon Redshift

4. Customer Data Platform (CDP)

Customer Data Platforms (CDPs) focus on consolidating customer data from multiple touchpoints to provide a unified customer view for personalized marketing and analytics.

Key Features:

• Unified customer profiles
• Real-time data processing
• Integration with marketing and analytics tools

Examples: Segment, Adobe Experience Platform, Twilio Segment

What Are the Benefits of Using a Data Platform?

A well-architected data platform provides numerous advantages to businesses, including:

• Centralized Data Management: Consolidates data from various sources into a single repository.
• Improved Data Quality and Consistency: Automated data validation and cleansing ensure accuracy.
• Scalability for Large Datasets: Cloud-native platforms scale automatically based on data volume.
• Real-Time Data Processing: Enables faster decision-making with low-latency pipelines.
• Enhanced Data Security and Compliance: Built-in encryption, access controls, and audit logs.
• Seamless Data Integration: Pre-built connectors for popular data sources and APIs.
• Advanced Analytics and Machine Learning: Native support for ML pipelines and predictive models.
• Cost Optimization through Cloud Services: Pay-as-you-go pricing models.
• Self-Service Capabilities for Business Users: User-friendly tools for data discovery and visualization.

Popular Data Platforms in the Market

Cloud-Based Solutions

• Snowflake
• Google BigQuery
• Amazon Redshift

Enterprise Data Platforms

• SAP Data Intelligence
• Oracle Autonomous Data Warehouse
• IBM Cloud Pak for Data

Self-Service & Open-Source Options

• Apache Spark
• Apache Kafka
• Airbyte

How to Choose the Right Data Platform?

Choosing the right data platform involves evaluating several factors:

Step 1: Identify Business Needs and Use Cases

Step 2: Evaluate Scalability and Performance

Step 3: Assess Security and Compliance Features

Step 4: Review Integration Capabilities

Step 5: Prioritize Self-Service Capabilities

Step 6: Consider Cost and Pricing Models

Step 7: Check Vendor Support and Community

Step 8: Test with Proof of Concept Projects

Conclusion

Data platforms play a pivotal role in modern data ecosystems by providing a unified framework for managing data across its entire lifecycle. With the growing importance of data-driven decision-making, businesses need robust data platforms to harness the full potential of their data.

Whether you’re looking for cloud-native solutions, enterprise-grade platforms, or open-source alternatives, understanding the various layers, benefits, and tools will help you make informed decisions. As data volumes and complexity continue to grow, data platforms will evolve to offer even more advanced capabilities, shaping the future of business intelligence and AI.

Frequently Asked Questions (FAQs)