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In the era of digital transformation, data is the lifeblood of any successful organization. The ability to harness data for informed decision-making, insights, and predictions has become paramount. However, traditional data management systems are no longer sufficient to meet the ever-evolving needs of businesses. This is where a modern data platform comes into play. In this article, we will explore the concept of a modern data platform and its key characteristics.

The 3 Characteristics of a Modern Data Platform

1. Enable self-service for a diverse range of users

Previously, data was siloed and accessible for the exclusive use of data professionals. A modern data platform removes barriers so that everyone within an organization is able to access and leverage data. It’s all about data democratization.

A modern data platform requires technology that is intuitive to use and features easy discovery capabilities. It should be as user-friendly as Google, allowing users to find what they need effortlessly. Users should also be able to perform basic analysis, understand the context of the data, and derive insights all on their own.

Some modern data platforms may also include AI-driven insights capabilities that can help users to derive valuable insights from data without any prior experience with data analysis. For example, platforms that include AI-driven insights can automatically identify patterns and trends in data, and then generate easy-to-understand reports and visualizations. This can help users to quickly and easily understand the data, and to identify opportunities and risks that they may not have noticed on their own.

2. “Agile” data management

Modern data platforms are governed by the twin principles of availability and elasticity.


This refers to data being readily available in a data lake or warehouse that separates storage and compute. By splitting these functions, modern data platforms enable the cheap storage of large amounts of data.


Elasticity refers to cloud-based compute functions that scale automatically to meet demand. For example, if the data team runs a lot of analysis on a Monday, then processing power will expand on that day to meet the need, before contracting to save on power.

3. Flexible, fast set-up, and pay-as-you-go

As previously mentioned, modern data platforms are mostly run on cloud infrastructures. Today’s cloud solutions are easy to set up, fast (thanks to data lakes or lake houses that reduce ETL time), and flexible so they evolve with future technologies. Plus, you don’t have to invest in building a massive on-premise infrastructure; instead, you pay for only what you need.

Now that we have a clear understanding of what a modern data platform is and its key characteristics, let’s delve into the key building blocks that make this concept a reality.

The Key Building Blocks of a Modern Data Platform

A modern data platform relies on several crucial building blocks to function seamlessly. Each of these building blocks plays a vital role in managing and utilizing data effectively.

Data Storage and Processing

Data storage and processing are at the core of a modern data platform. It involves the storage of vast amounts of data and the ability to process it efficiently.

Data Warehouse: A data warehouse is a centralized repository for storing structured data. It is designed for query and analysis and is typically used for business intelligence and reporting. Data warehouses often store historical data and are optimized for complex queries.

Data Lake: A data lake is a storage repository that can hold vast amounts of structured and unstructured data. Unlike data warehouses, data lakes can store raw data without a predefined schema, making them more flexible for various data types and big data analytics.

Data Mesh: Data mesh is a modern approach to data architecture that emphasizes decentralized data ownership and access. It involves breaking down data into smaller, manageable units, or “domains,” each with its own ownership and governance. This approach can improve data discoverability and access.

Data Lakehouse: A data lakehouse combines the features of data lakes and data warehouses. It aims to provide the flexibility of a data lake while also supporting the structured querying capabilities of a data warehouse. Data lakehouses are designed to handle both raw data and analytical workloads.

Some example tools that facilitate this Data Storage and Processing include:

Amazon S3: A highly scalable and durable object storage service.

Azure Data Lake Storage: Like Amazon S3, Azure Data Lake Storage is Microsoft’s cloud-based object storage service designed for storing and managing vast amounts of data.

Check out our case study on how HIDI reduced its monthly costs by $40,000 through a successful migration to Azure Data Lake Storage and the implementation of a modernized data platform.

Data Ingestion

Data ingestion is the process of collecting and importing data into the platform. Efficient data ingestion is essential for real-time data processing.

Batch Processing: Batch processing involves collecting and processing data in predefined, scheduled batches. Data is gathered over a specific time interval, and then the entire batch is processed at once. This method is suitable for non-real-time data analysis and is often used for historical or offline data.

Stream Processing: Stream processing, on the other hand, deals with real-time data ingestion and analysis. Data is ingested and processed as it arrives, allowing organizations to react to events as they happen. It’s commonly used for applications that require immediate insights, such as fraud detection, IoT (Internet of Things) data, and real-time monitoring.

Data Pipeline: A data pipeline is a structured and automated process for moving and transforming data from source to destination. It can include various steps like data extraction, transformation, and loading (ETL) to ensure data quality and consistency. Data pipelines are essential for data integration and management.

Some example tools that are widely used for data ingestion include:

FiveTran: A SAAS tool.

Singer: An Open-source tool.

Modern Data Transformation

Data transformation is the process of converting data from one format or structure into another to meet the needs of different systems or applications. Example tools for modern data transformation include:

Apache Spark: An open-source, distributed computing system.

Talend: A comprehensive data integration and transformation tool.          

Modern Business Intelligence and Analytics

This building block involves tools and technologies that enable organizations to gain valuable insights from their data. It’s all about turning raw data into actionable information. Some example tools for modern business intelligence and analytics include:

Tableau: A powerful data visualization tool.

Power BI: A business analytics service by Microsoft.

In conclusion, a modern data platform is a dynamic and versatile solution that empowers organizations to make data-driven decisions. It breaks down the barriers to data access, promotes agility in data management, and embraces the power of the cloud. With its key building blocks, it ensures that data is not just collected but transformed into valuable insights. As technology continues to evolve, the modern data platform will adapt and remain a critical asset in the ever-changing landscape of data management and utilization.