Data Platform: An Introduction

What is a Data Platform?

A data platform is a comprehensive/unified system for efficiently handling/managing, and analyzing large data amounts. It includes several components like databases, data lakes, and data warehouses to store structured and unstructured data. The platform streamlines the collection, management, and storage of data. This makes data accessible and usable for various purposes.

In addition to storing data, a data platform includes advanced data processing and analysis tools. It also contains engines for big data processing and machine learning algorithms. This allows companies to extract valuable insights from data, enhancing informed decision-making and strategic planning in several industries.

A data platform is the basis for modern data-driven initiatives for organizations to use their vast data reserves fully.

Architecture

A data platform architecture is a term for describing a data platform's underlying structure and layout. It comprises various technologies, tools, and methodologies for collecting, processing, storing, managing, and analyzing data.

Generally, the following components make up a data platform architecture:

1. Storage

Storage serves as the foundational element in the data lifecycle. Understanding the data use case and future retrieval needs is essential. Cloud-based object storage from major providers like Amazon S3, Google Cloud Storage, and Azure Blob Storage is prevalent. While on-premise alternatives exist, they are not as widespread, especially in architectures like Data Lakes.

2. Ingestion

Ingestion addresses the challenge of gathering data, often a significant bottleneck as data sources are usually beyond direct control. Tools such as Fivetran and open-source alternatives like Airbyte play a crucial role by providing out-of-the-box connectors to hundreds of data sources. This simplifies and streamlines the process of bringing external data into the system.

3. Transformation

Raw data needs transformation to be valuable for downstream use cases. BigQuery and Snowflake have emerged as powerful analytics engines and cornerstones of modern data infrastructure. These platforms facilitate the transformation of raw data into a usable format, enabling meaningful insights and analytics. Considerations include data destination, access frequency, volume, and real-time versus batch processing.

4. Serve

The ultimate goal of the data lifecycle is to extract value from the data. Business Intelligence (BI) tools such as Tableau and Qlik, which offer both on-premise and cloud solutions, play a crucial role in this stage. While these BI tools are well-established, the tooling around Machine Learning (ML) and Reverse ETL (Extract, Transform, Load) is still evolving and not as mature as BI tools. This stage involves considerations such as user needs, self-service capabilities, data discoverability, access control, and encryption during data transit.

5. Governance

As data volumes and sources continue to increase, governance becomes crucial for ensuring data quality, usability, and security. Traditional monitoring and logging tools may be sufficient, but emerging data governance providers are entering the market. These solutions aim to address the specific challenges related to data use cases. Considerations involve the number of data sources, teams, developers, and early testing of data to maintain high-quality standards throughout the lifecycle.

These components are interconnected to allow secure, reliable, and efficient data flow and processing from the ingestion point to the consumption point (just like a dashboard or report).

Data Platform vs Database

Data platforms and databases are significantly different. For instance, data platforms cover broader functions to manage the complete data lifecycle, while databases focus primarily on storing and retrieving structured data.

The table below is a comparison between data platforms and databases. It also highlights their key differences across various aspects.

Benefits of a Data Platform

A data platform improves organizational success by improving data management, analytics, and decision-making. This section will discuss some critical benefits of a data platform:

Organizational Data Management

A data platform is a unified hub for storing, organizing, and managing data. This approach streamlines data access, ensuring data consistency and reducing the risk of fragmented information across the organization.

Scalability

It allows the platform to adapt to growing organizational needs, including higher data volumes and increasing user demands. Whether handling small or big data, a well-designed data platform scales horizontally or vertically. This ensures optimal performance while data requirements continue to evolve.

Efficient Data Processing

Data platforms encourage efficient data processing through features like data normalization, transformation, and analytics. This efficiency creates faster insights, better decision-making, and increased ability to extract valuable information from raw data.

Cross-Functional Collaboration

Data platforms provide a unified basis for accessing and analyzing data. This is achieved by encouraging collaboration across different organizational teams and departments. This shared environment promotes a central understanding of corporate data, fostering collaboration among data scientists, analysts, and company stakeholders.

Data Security and Governance

Robust data security measures are part of data platforms. They enforce the protection of sensitive information, access controls, and data governance policies. This is essential for maintaining compliance with regulations safeguarding data integrity.

Real-time Insights

Many data platforms support real-time data processing and analytics. This enables organizations to get insights and make decisions based on the most up-to-date information. This is particularly valuable in changing business environments where timely decisions are vital.

Flexibility and Adaptability

Data platforms are made to handle diverse data types and sources. This offers flexibility in accommodating changing data formats and structures. Adaptability is crucial for managing effectively evolving business requirements and technological landscapes.

Data-Driven Decision-Making

The goal of a data platform is to empower data-driven decision-making. Establishments can make informed decisions, identify trends, and leverage opportunities by providing the tools and infrastructure for practical data analysis. This contributes to the overall business success.

Cost Efficiency

Cloud-based data platforms are cost-efficient by providing a pay-as-you-go model. This means organizations only pay for the resources they use. As a result, they avoid unnecessary costs and optimize data storage and processing expenses.

Innovation and Advanced Analytics

A well-implemented data platform enables organizations to explore innovative technologies. These innovative technologies include machine learning and artificial intelligence. They possess advanced analytics capabilities that allow for predictive modeling, automation, and discovery of valuable patterns within a given data set.

Challenges in Building a Data Platform

Setting up a data platform can be daunting and comes with its own set of challenges. Here's a brief list outlining some challenges in this process:

• Data Processing Choice: This deals with deciding where and how to process data, especially in data platform architecture.
• Data Centralization and Organization: Setting up a unified repository can be problematic when dealing with diverse data types.
• Complex Architecture: Designing a stable architecture for managing extensive data is challenging when setting up a data platform.
• Platform Integration with Existing Systems: This process requires enormous effort in planning and execution.
• Data Security and Privacy: The need to protect organizational intelligence from unauthorized handling poses a significant challenge to data security.
• Skill Gap: Companies must invest mainly in their personnel to close the skill gap in data engineering, science and platform administration.
• Scalability: This covers the need to accommodate future growth alongside the continuously increasing data volumes.
• Cost Management: Balancing hardware, software and maintenance costs leads to unprecedented organizational expenses that may hamper their financial reserves.
• Data Governance: Data governance policies and practices may not always favor tech establishments, and seeking data platform alignment with these policies is critical for ensuring compliance.

Examples of Providers & Solutions

Now that you know what data platforms are, it's time to look at some examples, their use cases and their scope.

Snowflake

Snowflake is a cloud-based data platform that offers a scalable and versatile solution for storing and analyzing data. Companies can keep and analyze large data volumes with Snowflake. This makes it useful for establishments needing flexible and efficient data warehouse solutions in the cloud.

Microsoft Azure Synapse Analytics

It was formerly known as Azure SQL Data Warehouse. Microsoft Azure Synapse Analytics is a cloud-based data platform that integrates data warehousing and extensive data analysis. It caters to businesses with several data needs. This leads to seamless data integration, storage, and analytical capabilities, making it perfect for companies seeking a comprehensive cloud data solution.

Apache Hadoop

Apache Hadoop is a big data platform for distributed storage and processing of large datasets. It is beneficial for organizations dealing with large amounts of unstructured data. It provides a framework that fosters efficient storage, retrieval, and analysis of diverse data types across a cluster of computers.

Tableau

Tableau is a popular data visualization platform. It enables users to convert complex datasets into interactive and understandable visualizations. It is also used for creating insightful dashboards and reports. This makes it an essential tool for organizations seeking to derive actionable insights from their data through user-friendly visual representations. 

How to choose the right Data Platform?

Choosing the right data platform is a decision that businesses cannot afford to take lightly.  The reason is that every company has different data platform needs. Also, such critical decisions depend on a multiplicity of factors to ensure the platform aligns perfectly with company goals.

Therefore, more prominent companies are likely to establish custom data platform solutions. Alternatively, they can invest in separate tools to match their desired capabilities. Conversely, small and medium-sized businesses can go for a full-stack platform. Regardless of which option a company chooses, some features must be considered. In this article, we recommend product categories with example use cases covering the data platforms.

Storage

Storage is the cornerstone of the data lifecycle - Knowing the use case of the data and the way you will retrieve it in the future is the first step to choosing the proper storage solutions for your data architecture.

Architectures like the Data Lake heavily depend on the major cloud providers’ object storage – on-premise alternatives exist, yet they are not as widespread as their cloud-based counterparts.

Here are what to consider:

1. Is it compatible with the architecture required to write and read speed?

2. Will storage create a bottleneck for downstream processes?

3. Will it handle the anticipated future scale?

4. Will downstream users be able to retrieve data in the required service-level agreement?

5. Are you capturing metadata about schema evolution, data flows, and data lineage?

6. Must schemas be enforced, or should they be flexible?

7. How are you handling regulatory compliance and data sovereignty?

8. Encrypt data at rest.

Ingestion

Ingestion is required to gather the data of need – ingestion represents the most significant bottlenecks in the data lifecycle as data sources are usually outside of control.

Tools like Fivetran or open-source alternatives like Airbyte have revolutionized data ingestion by providing out-of-the-box connectors to hundreds of data sources.

Here are what to consider:

1. What is the data’s destination after ingestion?

2. How frequently will the data be accessed?

3. What is the data’s typical volume upon arrival?

4. What is the data’s format, and can downstream storage and transformation handle the format?

5. Is real-time data ingestion required (streaming), or is batch ingestion good enough?

6. Does the source system push data, or is data being pulled from the source system?

7. Encrypt data in transit.

Transformation

Raw data must be transformed into something useful for downstream use cases – without proper transformation, data will sit inert and create any value.

BigQuery and Snowflake established themselves as the most powerful analytics engine and the cornerstone of modern data infrastructure.

Here are what to consider:

1. What is the data’s destination after ingestion?

2. How frequently will the data be accessed?

3. What is the data’s typical volume upon arrival?

4. What is the data’s format, and can downstream storage and transformation handle the format?

5. Is real-time data ingestion required (streaming) or is batch ingestion good enough?

6. Does the source system push data or is data being pulled from the source system?

7. Encrypt data in transit.

Serve

The last stage of the data lifecycle is to get value out of the data – data has value when it’s used for practical purposes.

BI tools like Tableau or Qlik are well established and offer on-premise solutions – tooling around ML and Reverse ETL is not yet as mature as the BI tools.

Here are what to consider:

1. Who will use the data being transformed and aggregated?

2. Do users need to run their analysis (selfservice), or are predefined reports sufficient?

3. Is the data discoverable?

4. Who should have access to the data?

5. Is multi tenancy required?

6. Are decisions automatically made on data?

7. Encrypt data in transit.

8. Test data as early as possible.

Governance

As data volumes and data sources keep increasing, data governance is crucial to ensure data quality, usability and security.

While traditional monitoring and logging tools might be sufficient, many new providers focusing on data use cases pour into the market – their solutions have yet to prove their product-market fit.

Here are what to consider:

1. How many data sources?

2. How many teams and developers working with the data sources?

Orchestration

The more jobs run, the more important an orchestration tool becomes – without such a tool handling lots of jobs will get unmanageable.

Airflow remains the top dog among orchestration tooling, yet contenders catch up by providing serverless solutions.

Here are what to consider:

1. Does the system require to trigger single jobs or multiple jobs dependent on each other?

2. Jobs being dependent on each other might require an event-driven design

Conclusion

Data platforms are central to managing and deriving value from data in this data-dependent society. They provide the essential infrastructure and tools for handling, processing, and analyzing data. They have also contributed to the advancement in meeting the increased demands of modern data workloads. Overall, this blog has covered the exciting world of data platforms, exploring their key components, capabilities, and evolution.