Modernizing Your Data Platform: An Introductory Overview — Part 1

In today’s business landscape, having full control over your data is crucial for making informed decisions. To truly become a data-driven company, you need to construct a robust ecosystem for data analytics, processing, and insights. This is imperative given the diverse range of applications like websites, dashboards, mobile apps, machine learning models, and distributed devices that generate and consume data.

Various divisions within an organization, including finance, sales, marketing, operations, and logistics, all require data-driven insights. However, data use cases vary in terms of transactional, the required throughput, permissions for data access, and the volume of transactions or queries they need to support.

This article aims to provide an introductory overview of modernizing your data platform. We’ll delve into what a data platform is, what it requires, and why traditional data products fall short. Additionally, we’ll explore technology trends in data analytics and AI, along with strategies to build data platforms for the future using public cloud services.

What is a Data Platform and Why do Organizations Need It?

In the traditional setup, organizations often rely on separate solutions for managing different data services, creating silos within the organization. These isolated systems operate independently and hinder efficient collaboration, rendering the data within them less insightful. To enhance enterprise intelligence, securely sharing data across business units is paramount.

Moreover, relying on custom-built solutions for various parts of the organization leads to challenges in business continuity and disaster recovery planning. Diverse environments chosen by different parts of the organization complicate ensuring high availability, disaster recovery, and privacy. In such scenarios, the answer lies in developing a unified data platform, particularly a cloud data platform, designed to facilitate analytics and machine learning consistently and reliably across an organization’s data.

Breaking Data Silos with Data Movement Tools

To combat the challenge of disparate data management solutions, organizations often resort to data movement tools, such as Extract Transform Load (ETL) applications. These tools enable data transformation and transfer between different systems, consolidating data to create a unified source of truth. For instance, an ETL tool can regularly extract recent transactions from a database and archive them into an analytics store, streamlining analytics processes.

The central analytics store that captures all the data across the organization is referred to as either a data warehouse or a data lake depending on the technology being used. A high-level distinction between the two approaches is based on the way the data is stored within the system: if the analytics store supports Standard Query Language (SQL) and contains governed quality-controlled data, it is referred to as a data warehouse.

If instead it supports tools from the Apache ecosystem (such as Apache Spark) and contains raw data, it is referred to as a data lake. Terminology for referring to in-between analytics stores (such as governed raw data or ungoverned quality-controlled data) varies from organization to organization — some organizations call them data lakes and others call them data warehouses.

Challenges and Drawbacks of Data Movement Tools

However, relying solely on data movement tools poses challenges:

1. Latency: ETL tools introduce delays in data processing, potentially rendering the data stale for analytics purposes.
2. Bottleneck: Building and maintaining ETL tools require specialized programming skills, creating bottlenecks in data engineering teams.
3. Maintenance: Routine running of ETL tools necessitates regular maintenance and updates, adding complexity to the system.
4. Change Management: Changes in the source schema require alterations to the ETL tool, making changes cumbersome and potentially disrupting data flow.
5. Data Gaps and Governance: Escalating errors and managing inconsistencies in data quality and governance pose significant challenges.

The Perils of Data Ecosystem Complexity

The proliferation of storage systems and custom data management solutions tailored to various downstream applications results in a chaotic data ecosystem. This complexity leads to challenges such as:

• Need for adequate scalability to accommodate growing business needs and digital initiatives.
• Creation of multiple data silos due to the need for separate data lakes, data warehouses, and specialized storage for different data science tasks.
• Limiting data access due to performance, security, and governance constraints.
• Difficulties in license renewals and managing expensive support resources.

Centralizing Control: A Double-Edged Sword

To address the challenges posed by scattered data and disparate data systems, some organizations opt for centralization of data management under the IT department’s control. However, this centralized control brings its own set of challenges:

• Diverse Technologies: IT departments often lack the skills required to manage the diverse set of technologies involved in data silos.
• Analytical Challenges: Accessing the right data becomes difficult, leading to unnecessary ETL tasks and limitations on data access.
• Business Limitations: Balancing data access and quality becomes challenging, affecting the business’s ability to make informed decisions.

Despite the challenges, several organizations have adopted centralization, which sometimes results in frustration and tensions for business users due to delays in accessing essential data. This has also led to the emergence of shadow IT, exacerbating the problem of siloed data.

The Evolution of Data Platforms: From Warehouses to Lakes and Beyond

In the fast-paced world of data management, the traditional siloed approach of centrally managed data systems created significant challenges and overhead for IT departments. This led to the development of data warehouses, which were pivotal in allowing businesses to structure their data and gain valuable insights. However, data warehouses faced limitations in terms of capacity and scalability. This drove the emergence of data lakes, especially with the advent of Big Data solutions based on the Apache Hadoop ecosystem. In this article, we’ll explore the journey from data warehouses to data lakes and the modern cloud-based solutions that address the limitations of traditional on-premises setups.

Data Warehouses: A Powerful Solution

Data warehouses revolutionized data management by allowing business users to design and deploy structured data models. They were SQL-based systems, making them accessible and comfortable for many business users. Over the years, various technologies, such as Oracle, Teradata, and Vertica, were employed to implement data warehouse solutions. However, on-premises data warehouses had limitations, particularly in scaling infrastructure and managing costs.

The Rise of Big Data and Data Lakes

The explosion of data in terms of volume, velocity, and variety led to the rise of Big Data solutions, notably the Apache Hadoop ecosystem. Hadoop introduced distributed data processing through horizontal scaling, providing a cost-effective alternative for some traditional data warehouse workloads. This paved the way for data lakes, a central repository for analytics workloads and self-service analytics across organizations. The Hadoop OSS ecosystem grew with various data systems and processing frameworks, addressing the burgeoning data needs.

Cloud: A Paradigm Shift for Data Platforms

Running data warehouse and data lake technologies on-premises posed challenges, especially with scaling and operational costs. Organizations began looking towards the cloud, particularly the public cloud, as a viable solution. The cloud offered benefits like reduced costs through pay-per-use models, faster innovation with best-of-breed technologies, and seamless scaling by bursting into the cloud. It also ensured business continuity, disaster recovery, and efficient data management. Cloud data platforms promise centralized governance, increased productivity, enhanced data sharing, extended access, and reduced latency of data access.

Converging Data Warehouses and Data Lakes: The Lakehouse Approach

Recognizing the drawbacks of traditional data warehouses and data lakes, a new approach emerged — the lakehouse architecture. This architecture combines the benefits of both data warehouses and data lakes, offering inexpensive, virtually unlimited, and scalable storage. It allows for stateless, resilient computing and supports ACID-compliant storage operations. However, it is a technological compromise, facing challenges in SQL efficiency compared to native data warehouses.

Unlocking the Future with Data Mesh

Acknowledging the need for a paradigm shift, the data industry is moving towards a Data Mesh approach. Data Mesh treats data as a product and promotes decentralized data ownership. It involves organizing data around domains, akin to microservices, allowing for more efficient data access and usage across domains. Data Mesh empowers organizations to overcome data silos and optimize the entire data stack, enabling a deeper understanding of the business and driving innovation.

Let's see more about this approach in Part II of the Architecting for Data and ML Platforms series.

Where to Next?

In conclusion, modernizing the data platform is essential to break down data silos and empower organizations to harness the full potential of their data for improved business decisions.

In subsequent chapters, we will delve deeper into essential concepts and strategies, such as data silos removal, convergence of data lake and data warehouse, hybrid architecture, and integrating machine learning in the enterprise, to guide organizations in designing efficient modern data platforms. Stay tuned for a comprehensive exploration of these vital topics.

Wait for part two!