5 Pillars of a Modern Data Storage Platform

What Is a Data Platform? 

A data platform is an integrated solution that collects, manages, and analyzes data from various sources. It serves as the central repository for storing data, making it easily accessible for users and applications. Modern data platforms handle diverse data types such as structured, unstructured, and semi-structured data, enabling organizations to extract insights and drive decision-making.

Data platforms streamline data workflows, from ingestion and storage to processing and analysis. Organizations rely on them to unify disparate data sources, ensure data quality, and provide analytics capabilities. By automating data management tasks, data platforms free up resources and allow teams to focus on deriving value from the data.

This is part of a series of articles about AI infrastructure

In this article:

• The Evolution of Data Platforms
• Pillars of a Modern Data Platform
• Building a Data Platform: A Strategic Approach

The Evolution of Data Platforms 

Data platforms have evolved over the past few decades, driven by advancements in technology and the growing importance of data in business decision-making.

Early data management systems

In the 1970s and 1980s, the first generation of data platforms consisted of basic database management systems (DBMS). These systems were primarily focused on storing and retrieving structured data efficiently. Relational databases like Oracle, IBM DB2, and Microsoft SQL Server became prominent, providing SQL-based query capabilities that revolutionized data storage and retrieval.

Enterprise data warehousing

The 1990s saw the advent of data warehousing. Organizations began to accumulate vast amounts of data, requiring specialized systems to store and analyze this data. Data warehouses provided a centralized repository for structured data, optimized for complex queries and reporting. ETL processes became essential, extracting data from operational systems, transforming it into a consistent format, and loading it into the warehouse.

Big data era

The 2000s marked the rise of big data, driven by the explosion of data from the internet, social media, and IoT devices. Traditional data warehouses struggled to handle the volume, velocity, and variety of big data. This era saw the emergence of data lakes, which allowed organizations to store vast amounts of raw data in its native format. Technologies like Hadoop and NoSQL databases (e.g., MongoDB, Cassandra) enabled scalable storage and processing of unstructured and semi-structured data.

Modern data platforms

In the 2010s and beyond, the concept of the modern data platform emerged, integrating the best aspects of data warehouses and data lakes. Data lakehouses and cloud-native platforms like AWS, Azure, and Google Cloud Platform provide scalable, flexible, and cost-effective solutions for managing diverse data types. These platforms support analytics, machine learning, and real-time data processing, enabling organizations to derive actionable insights more efficiently.

5 Expert Tips that can help you make better use of modern data storage platform

Jon Toor, CMO

With over 20 years of storage industry experience in a variety of companies including Xsigo Systems and OnStor, and with an MBA in Mechanical Engineering, Jon Toor is an expert and innovator in the ever growing storage space.

Embrace data virtualization: Implement data virtualization tools to abstract data access from physical storage. This simplifies data integration and provides real-time data access without physically moving data, reducing latency and improving performance.

Leverage containerization for scalability: Use containerization technologies like Docker and Kubernetes to deploy data platform components. Containers provide scalability, isolation, and portability, facilitating seamless deployment and management across different environments.

Focus on data minimization: Implement data minimization principles to reduce the amount of data collected and retained. This not only improves data quality and governance but also minimizes storage costs and potential risk exposure.

Utilize in-memory processing: Integrate in-memory processing technologies like Apache Ignite or SAP HANA to accelerate data analytics and reduce query response times. This is particularly beneficial for real-time data processing and high-performance applications.

Automate data pipeline orchestration: Use orchestration tools like Apache Airflow or Prefect to automate and manage complex data workflows. Automation ensures consistency, reduces manual errors, and improves the efficiency of data processing tasks.

Pillars of a Modern Data Platform

1. Data Storage and Processing

Data Warehouses

Data warehouses serve as centralized repositories optimized for the storage, retrieval, and analysis of large volumes of structured data. These systems facilitate complex queries and reporting, enabling organizations to derive insights from historical data. This structured approach ensures high performance, data integrity, and reliability.

Data warehouses typically organize data into schemas, such as star or snowflake schemas, that are optimized for read-heavy operations. They support online analytical processing (OLAP), which allows users to perform multidimensional analysis on large datasets efficiently. Another important aspect of data warehouses is that they support extract, transform, load (ETL) processes, which are crucial for integrating data from disparate sources into a unified repository. 

Traditional data warehouses, including Oracle, IBM DB2, and Microsoft SQL Server, have been the backbone of business intelligence (BI) and reporting for decades. These platforms handle structured data from various operational systems and transform it into a consistent format for analysis. 

Data Lakes

Data lakes provide a more flexible and scalable solution for storing large volumes of data in its raw, unprocessed form. Data lakes support analytics, including machine learning, real-time processing, and big data analytics, providing a versatile foundation for extracting insights from diverse datasets.

Unlike data warehouses, which require predefined schemas and are optimized for structured data, data lakes can accommodate structured, semi-structured, and unstructured data without needing prior data modeling. This flexibility allows organizations to ingest data from a wide variety of sources, including logs, sensor data, social media feeds, and more, at high velocity and volume.

Technologies like Hadoop distributed file system (HDFS) and cloud-based storage solutions such as Amazon S3 are commonly used to build data lakes. These systems offer cost-effective storage and scalable computing capabilities, enabling organizations to process and analyze vast amounts of data using distributed computing frameworks like Apache Spark. 

Data Lakehouse

The data lakehouse is an architectural paradigm that merges the strengths of data warehouses and data lakes, providing a unified platform for managing diverse data types and analytical workloads. 

This approach combines the structured data management and high-performance querying capabilities of data warehouses with the scalability and flexibility of data lakes. Data lakehouses handle both structured and unstructured data, enabling organizations to perform analytics without the need for separate systems.

Platforms like Databricks and Snowflake exemplify the data lakehouse architecture, offering integration of data storage, processing, and analytics. These platforms support SQL-based querying and BI tools, as well as analytics and machine learning frameworks. By providing a single environment for all data-related tasks, data lakehouses reduce complexity, streamline data workflows, and lower operational costs.

Data Mesh

Data mesh is a decentralized approach to data architecture that emphasizes domain-oriented data ownership and management. Instead of relying on a central data team to manage the entire data platform, data mesh advocates for distributing data responsibilities to domain-specific teams. Each team is responsible for its own data products, ensuring better alignment with business needs and greater accountability for data quality and usability.

The principles of data mesh include data as a product, self-serve data infrastructure, and federated computational governance. This approach empowers domain teams to develop, manage, and consume data products independently, fostering agility and scalability. 

By breaking down data silos and promoting collaboration across teams, data mesh enables organizations to leverage data more effectively and drive innovation. Implementing a data mesh requires a cultural shift towards data democratization and standardized data practices.

2. Data Ingestion

Data ingestion is the process of collecting, importing, and processing data from various sources into a centralized data platform. It is a critical first step in the data lifecycle, ensuring that data is available for subsequent processing and analysis. Effective data ingestion pipelines handle data validation, error handling, and transformation, ensuring that ingested data is clean, consistent, and ready for analysis.

Data ingestion can occur in batch mode, where data is collected and processed in large chunks at scheduled intervals, or in real-time, where data is ingested and processed continuously as it is generated.

Batch data ingestion tools, such as Apache Nifi and Talend, are used for integrating large volumes of data from disparate sources, including databases, files, and APIs. Real-time ingestion tools, like Apache Kafka and Amazon Kinesis, enable the continuous flow of data from streaming sources, such as IoT devices, social media, and log files. 

3. Data Transformation

Data transformation involves converting raw data into a structured and analyzable format. This process includes data cleansing, enrichment, normalization, and aggregation to ensure that data is consistent, accurate, and suitable for analysis. Transformation is a crucial step in the data workflow, as it directly impacts the quality and reliability of the insights derived from the data.

Techniques such as extract, transform, load (ETL) and extract, load, transform (ELT) are commonly used for data transformation. ETL processes involve extracting data from source systems, transforming it according to business rules, and loading it into a target repository, such as a data warehouse. ELT processes load raw data into the target repository first and then apply transformations within the repository. Tools like Apache Spark, dbt (data build tool), and Informatica provide capabilities for performing large-scale data transformations efficiently.

Data transformation ensures that data is in a consistent format, removing duplicates, correcting errors, and enriching it with additional context or metadata. This process enhances data quality, making it reliable for business intelligence (BI) reporting, analytics, and machine learning applications. 

4. Business Intelligence and Analytics

Business intelligence (BI) and analytics are the primary functions enabled by modern data platforms, transforming raw data into actionable insights that drive decision-making. BI tools, such as Tableau, Power BI, and Looker, provide visualization capabilities, enabling users to create interactive dashboards, reports, and charts that communicate complex data trends and patterns clearly and effectively.

Advanced analytics goes beyond traditional BI by leveraging statistical models, machine learning algorithms, and data mining techniques to uncover hidden patterns, predict future outcomes, and recommend actions. Predictive analytics uses historical data to forecast future events, while prescriptive analytics suggests optimal courses of action based on data-driven insights. Platforms like SAS, R, and Python, along with cloud-based machine learning services from AWS, Azure, and Google Cloud, offer tools for building and deploying analytical models.

5. Data Observability

Data observability refers to the ability to monitor, track, and understand the state of data throughout its lifecycle within a data platform. It involves implementing tools and practices that provide visibility into data pipelines, ensuring that data flows smoothly from source to destination, and that any issues are promptly identified and resolved.

Observability solutions like Monte Carlo, Datadog, and OpenTelemetry enable continuous monitoring of data pipelines, providing real-time alerts and diagnostics for data anomalies, performance bottlenecks, and pipeline failures. These tools offer features such as data lineage tracking, which maps the journey of data from its origin through various transformations to its final destination. Data lineage enhances transparency, helping teams understand the impact of changes and trace the root causes of data issues.

Data observability also facilitates compliance with data governance policies and regulatory requirements, providing a comprehensive view of data handling practices. Investing in data observability practices helps organizations build trust in their data and maximize the value derived from their data assets.

Building a Data Platform: A Strategic Approach 

Engaging Subject Matter Experts (SMEs)

Incorporating SMEs into the development of a data platform is crucial for success. SMEs possess in-depth knowledge of the specific domains and can provide insights into the data requirements, sources, and usage patterns within their areas of expertise. Their input ensures that the data platform aligns with business needs and facilitates meaningful analysis.

SMEs assist in identifying critical data elements and defining data models that reflect real-world entities and relationships accurately. They also play a pivotal role in validating the data and ensuring its relevance and accuracy. By engaging SMEs early in the process, organizations can avoid common pitfalls related to data interpretation and usability, ultimately leading to a more functional data platform.

Focusing on People and Processes

Building a successful data platform involves more than just technology; it requires a focus on people and processes. Organizations must foster a data-driven culture where employees are encouraged to leverage data in their decision-making processes. Training and support are essential to help users understand and utilize the data platform effectively.

Establishing clear processes for data management, including data governance, stewardship, and compliance, is vital. These processes ensure that data is consistently handled, maintained, and protected across the organization. Implementing standardized workflows and best practices for data ingestion, transformation, and analysis helps maintain data quality and reliability, enabling teams to trust the data they work with.

Building Incrementally

An incremental approach to building a data platform allows organizations to manage complexity and reduce risks. Instead of attempting to deploy a solution all at once, organizations can start with a minimal viable product (MVP) that addresses the most critical data needs. This MVP can be gradually expanded by adding new features, data sources, and capabilities over time.

This iterative method enables continuous feedback and improvement, ensuring that the platform evolves in response to user needs and technological advancements. It also allows for better resource allocation and project management, as teams can focus on delivering tangible results in manageable phases. By building incrementally, organizations can achieve early wins and demonstrate the value of the data platform, garnering further support and investment.

Emphasizing Data Quality and Governance

Data quality and governance are foundational to the effectiveness of any data platform. High-quality data is accurate, complete, consistent, and timely, providing a reliable basis for analysis and decision-making. Implementing data quality management practices involves regular data profiling, cleansing, and validation to detect and rectify errors.

Data governance establishes the policies, procedures, and standards for managing data assets. It defines roles and responsibilities, ensuring accountability for data stewardship and compliance with regulations. Effective governance also includes data lineage tracking, which provides visibility into the data’s origins, transformations, and usage, thereby enhancing transparency and trust.

Related content: Read our guide to AI data platform (coming soon)

On-Premise Enterprise Data Storage with Cloudian

Cloudian HyperStore is an on-prem, enterprise storage solution that uses a fully distributed architecture to eliminate single points of failure, and enable easily scalability from hundreds of terabytes to exabytes. It is cloud native and fully compatible with the Amazon S3 API. 

The HyperStore software implementation builds on three or more distributed nodes, with data replication and erasure coding capabilities for high availability. It lets you add as many storage devices as needed, and the additional devices automatically join an elastic storage pool.

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