Introduction to Data Platform
A Data Platform is a centralized system that provides an integrated and scalable solution for managing various types of data such as structured, semi-structured, and unstructured data. It consists of different tools, technology, and infrastructure that are used for meeting an organization’s need for ingestion, preparation, storage, delivery, governance, compliance as well as security of the data. A data platform manages a variety of structures of data across the enterprise that includes IT and Technical operations, various lines of business, and data coming from internal and external clients.
A data platform is part of the modern data stack, as it consists of different techniques and tools that are developed and supported by different vendors. It mainly consists of data storage, data processing, and data analysis layers.
Importance of Data Platform
There are many reasons why organizations need a data platform. Below are some of the reasons for that.
Data Management: It helps in managing different types of structured, semi-structured, and unstructured data in the meantime ensuring data quality, consultancy, and accuracy of the data. It helps the organization to organize and categorize data so that it becomes accessible and usable for everyone.
Data Processing: It enables organizations to process large columns of data by providing different tools and infrastructure to ingest, transform, cleanse, and enrich the data in the same ecosystem.
Scalability: It enables the organization to scale its infrastructure with the increase in the volume of the input data. It provides the ability for the organization to add new data sources, increase the storage capacity, and handle the data processing demands.
Data Integration: It allows the organization to integrate a variety of data like JSON, XML, database, stream data, etc. from different sources into a single system or record or single system of truth
Analytics and Insight: It helps in performing analytics and generating insights from given data. There are various tools provided by a data platform, that can be used to explore the data visualization report and enable organizations to make data-driven decisions.
Data Platform Layers
A data platform consists of multiple layers, depending upon the set of functions and organizations where it’s being used. Even though the exact layers vary depending on the specific platform, some of the common layers include.
- Data Ingestion Layer
This platform in Data platform is responsible for ingesting data from diverse sources such as files, databases, streaming platforms, or API (Application Programming Interface). Data collected in this layer is gathered, validated, and transformed into an efficient format that can be used by all the platforms.
- Data Storage Layer
In this layer, ingested data is stored persistently and efficiently. This layer can store data in various types of data stores such as relational(SQL) and non-relational databases (NoSQL), data lakes, and data warehouses, and is flexible enough depending upon the nature of the data and use case.
- Data Processing Layer
In this layer, data is processed to extract insights or transform them into a format that is suitable for downstream applications. Various tools support the processing of different types of workloads, such as batch processing tools (Apache Spark and Hadoop), stream processing tools (Apache Kafka, Apache Flink), or machine learning frameworks(e.g. TensorFlow, PyTorch).
- Data Governance Layer
This layer ensures that data within the data platform is accurate, secure, and compliant with the local government regulations and policies. It provides various functions to manage data quality, metadata, access control, and auditing.
- Data Analytics Layer
This layer provides insights and intelligence from the ingested and processed data. It also provides various functions such as data visualization, reporting, dashboards, data discovery from existing data, and various other tools for data science.
- Data Application Layer
This layer enables the delivery of data-driven applications and services to end users. It provides various functionality for application development, API management, and integration with another enterprise system.
When the data platform is designed properly, it provides organizations with a unified view of the data and enables them to make data-driven decisions.
Popular Data Platform
There are many data platforms in the market, with each of them having its strengths and weaknesses. The below list provides some of the popular data platforms that in running in on-premise or cloud environments.
Cloudera/Hortonworks Hadoop Platform
It is an enterprise-level data platform that provides a unified experience for storing, processing, and analyzing data across multiple cloud and on-premise environments. It comprises various big data components like Apache Hadoop, Hadoop Distributed File System, and Apache Spark.
Apache Hadoop Framework
It is an open-source big data analytics tool used for distributed storage and processing of large datasets.
Apache Spark Framework
It is an open-source and distributed computing system that provides the capability for fast, in-memory processing and analytics of large data sets.
Amazon Web Services(AWS) Data Platform
AWS provides a variety of cloud-based services for big data processing and analytics. Some important AWS data platform services are listed below.
- Amazon S3 for storage
- Amazon EMR for Hadoop and Spark processing
- Amazon Redshift for Data Warehousing
- Amazon Kinesis for Real-time Analytics
- Amazon QuickSight for Dashboard
- AWS Lake Foundation for Data Lake
Microsoft Azure Data Platform
Microsoft Azure Data Platform is a cloud-based data platform service provided by Microsoft for its customers. Azure provides various suites of cloud-based solutions for data platform needs. Some important Azure data platform services are listed below,
- Azure HD Insight for Hadoop Spark Processing
- Azure Synapse Analytics for Data Warehousing
- Azure stream analytics for processing real-time data
- Azure Data Lake
Google Cloud Platform(GCP)
Google Cloud Platform Data Platform is a collection of cloud-based services provided by Google Cloud Platform for building, managing, and analyzing data. It consists of several services such as Google Big Query, Google Cloud Storage, and Google Cloud Dataflow
Snowflake Data Platform
Snowflake is a cloud-based data warehousing platform that provides a managed, scalable, and secure data warehouse for processing and analysis of any volume of data. It is one of the most popular cloud-based data analytics platforms.
Databricks Unified Data Analytics Platform
Databricks is a unified analytics cloud-based platform that provides the capability for big data processing and analytics for data engineering, data science, and machine learning tasks. It includes various services such as Apache Spark, Delta Lake, and Machine Learning Flow for various engineering needs.