What is a Modern Data Platform?
While data platforms are not new, they have been constantly evolving for the last two decades. A modern data platform is a set of cloud-based software products and tools that enable the collection, cleaning, transformation and analysis of data. A modern data platform is designed to be scalable, proactive, and democratic, allowing it to be flexible for new technologies and changing demands of data teams.
The Foundations of a Modern Data Platform
A modern data platform, commonly known as a modern data stack, is structured around five key foundational layers: data storage and processing, data ingestion, data transformation, business intelligence (BI) and analytics, and data observability.
Each layer plays a crucial role in ensuring seamless data flow, from collection to insight generation and monitoring.
According to our Head of CoE and AI, Gaurav Dhing, “Adopting a modern data platform on the cloud is not just a shift in technology; it's a strategic move that enables businesses to stay ahead by accessing real-time insights, anywhere and anytime.”
1. Data Storage and Processing
Today, businesses can choose from a variety of storage formats including data warehouses, data lakes, data lakehouses, and data mesh. These architectures address different use cases, with emerging solutions like lakehouses and data mesh gaining in popularity due to their integrated and decentralised capabilities.
a. Data Warehouse
Data warehouses are built to store structured data that is optimised for analytics and reporting. They follow a schema-on-write model, which means data is cleaned and structured before it’s stored, which boosts the speed of a query and its analytical performance.
Ever since businesses have begun shifting their on-platform data warehouses to the cloud, there has been no looking back. Cloud-based data warehouses have stayed as the preferred data storage solution for their optimisation ability and faster speeds.
Some of the popular cloud warehouse platforms are: Amazon Redshift, Azure Synapse Analytics, Google BigQuery
b. Data Lakes
Data lakes offer flexibility by storing vast amounts of raw, unstructured, and semi-structured data. With a schema-on-read model, data lakes enable businesses to store data in its native format without the need for transformation upfront. This makes data lakes an attractive option for businesses wanting to quickly store large scale data, with the ability to structure it later based on specific analytical needs.
c. Data Lakehouse
A data Lakehouse merges the flexibility of data lakes with the data management capabilities of data warehouses, offering a unified platform for both structured and unstructured data. By combining data storage and computing, data lakehouses help businesses minimise data transfers and speed up the process of gaining insights.
Data lakehouses are built to support both business intelligence (BI) and machine learning, making them versatile for a range of analytical needs. They are equipped with advanced query engines that enable fast and powerful SQL queries, enhancing their performance for large-scale data analysis.
d. Data Mesh
Data mesh takes a decentralised approach to data architecture by distributing data ownership across various business domains. This helps businesses create domain-specific data management where each team or department is responsible for managing its own data, while ensuring that the entire organisation is governed by unified data governance practices.
This makes data mesh ideal for companies that need scalable, cross-functional data management systems to support a diverse range of data domains.
2. Data Ingestion
Data ingestion is the process of moving data from various sources into a central storage system to make it available for use in the future, whether that’s for record-keeping or deeper analysis. It’s a crucial second layer of a modern data platform. To put it simply, data ingestion brings data from places like databases, SaaS platforms, IoT devices, and mobile applications into a unified system where it can be accessed and processed.
To understand how critical this layer is for businesses, we have to understand that the quality of a business' analytics hinges on how well their data is ingested and integrated. The entire analytical pipeline could suffer if the data ingested is flawed or outdated. This is especially problematic with big data, where the volume and speed of data require efficient handling from the start.
There are two primary methods of data ingestion: batch processing and real-time processing (also known as streaming).
Batch processing is the traditional method, where data is collected in batches and processed on a schedule or when certain conditions are met. It’s suitable for situations where real-time updates aren’t critical, as it’s more cost-effective and less resource intensive.
Real-time processing, on the other hand, involves capturing and processing data as soon as it is generated. While this method allows for instant insights and reactions, it comes with a higher price tag and requires continuous monitoring.
With modern data platforms, the ingestion process is generally automated, unlike older systems where much of this work used to be manual.
3. Data Transformation
Data transformation is the crucial step of converting data from one format into a format that’s suitable for analysis and decision-making. It involves modifying the structure, format, or values of the data so it can be more easily used for reporting or analytics projects. This transformation can take place either before or after the data reaches its storage location, depending on the type of pipeline in use.
Modern data platforms have changed the way the ETL (Extract, Transform, Load) processes are executed. Unlike traditional ETL workflows, where data was transformed before being loaded into storage, modern platforms leverage the cloud to enhance both the speed and flexibility of this process. In the cloud environment, ETL workflows can handle massive volumes of data, processing it in real time or in batches, depending on business needs. These platforms utilise the cloud’s elastic compute resources to scale whenever needed, ensuring that even the most complex transformations are handled quickly and efficiently.
There are significant advantages of data transfromation:
Improved Usability: By standardising and structuring the data, it becomes more easily accessible and valuable, allowing teams to extract insights from what would otherwise be raw and unusable information.
Enhanced Data Quality: Transformation helps in cleaning the data by identifying errors, correcting inconsistencies, and filling in gaps, for more accurate datasets.
Structured and Organisation: Structured and transformed data is easier for both humans and machines to process, speeding up analysis and decision-making.
4. Business Intelligence and Analytics
The fourth layer of a modern data platform is focused on business intelligence (BI) and analytics tools that transform data into meaningful insights through detailed analysis and easy-to-understand visuals such as charts, graphs, and dashboards. By leveraging BI and analytics, teams across an organization (from data scientists to business leaders) can uncover trends, track performance, and make more informed strategic decisions.
Today, both business analytics (which focuses on using historical data for decision-making) and data analytics (which can also handle more complex, predictive insights) play a key role in how businesses operate.
BI tools like Tableau, Power BI allow users to analyse and present data visually, ensuring even non-technical stakeholders can interpret complex datasets and gain actionable insights.
5. Data Observability
The fifth and final foundational layer of a modern data platform is data observability, which refers to an organisation’s ability to monitor the health and performance of its data systems. Unlike basic monitoring, data observability offers a comprehensive view of how data is flowing through the system and identifies potential issues like delays, anomalies, or data quality concerns in real time. This is especially important in complex, distributed systems where tracking data across different pipelines and processes is crucial.
Data observability enables teams to proactively catch and fix problems before they impact business decisions. By implementing features like SLA tracking (which measures data performance against standards), monitoring (real-time dashboards), logging (historical data tracking), and alerting (automated warnings for anomalies), organisations can ensure their data is accurate, timely, and reliable. This layer also involves analysing data to identify trends and predict potential issues.
In modern platforms however, observability goes beyond individual teams or departments. It promotes transparency and collaboration across the organisation, ensuring that data is shared, tracked, and accessible to those who need it. For businesses using decentralised systems like data mesh, observability becomes even more critical, enabling different teams to work with shared data while ensuring control over its quality and integrity.
Make you way to the Cloud
Building a modern data platform is key to unlocking actionable insights, driving innovation, and staying competitive. With a cloud-based infrastructure that scales effortlessly and supports evolving needs, businesses can future-proof their data strategies. However, the path to building or migrating to a modern data platform comes with its challenges. This is where Exponentia.ai shines. With deep expertise in cloud migration, data engineering, and analytics, Exponentia.ai is the ideal partner to help businesses navigate the complexities of modern data platforms.
Let Exponentia.ai guide your business in harnessing the full potential of a modern data platform.