The emergence of OLTP and OLAP systems

OLTP and OLAP systems have long been regarded as foundational for enterprise data management. OLTP systems handle real-time transactions, while OLAP systems focus on long-term data analysis, creating a clear separation between operational and analytical workloads. However, this division brought challenges, including the need for constant ETL processes to transfer data between the two systems, which resulted in latency and inconsistencies between operational and analytics data.In the following subsections, we will dive deeper into the roles and limitations of OLTP and OLAP systems, setting the stage for understanding how modern data architectures such as data lakes and lakehouses address these issues.

Data lakes – centralized data storage for a new era

Data lakes are centralized storage facilities for raw data in native formats. Thus, they are designed to handle large volumes from sources such as social media, IoT devices, and sensors, and often run on top of distributed file systems such as Hadoop, HDFS, or cloud-based object storage such as Amazon S3.With big data solutions starting to see demand, enterprises needed a system to capture, process, and analyze unstructured and semi-structured data coming from sources such as logs, sensors, and social media. Over time, data lakes became systems that store raw data natively in their format with not-so-stringent schema enforcement.Data lakes leverage distributed storage systems designed to scale with volume. This flexibility in scaling lets organizations store many types of data formats in a single location, potentially unlocking advanced analytics, machine learning, and AI capabilities not afforded by the rigidity of traditional...

The emergence of lakehouse architecture

Lakehouse architecture combines the advantages of data lakes and data warehouses. It introduces a unified platform for both transactional and analytical workloads, addressing the need for scalable, flexible, and high-performance data processing.Apache Iceberg, Apache Hudi, and Delta Lake – three main open source projects driving the evolution of lakehouse architecture together with their respective communities – have contributed innumerable feature additions such as data governance, ACID transactions, and query optimization to data lakes, thereby enabling the lakehouse to prove itself a real solution for the unification of data infrastructure for any organization.

An introduction to data lakehouse

The data lakehouse architecture has shifted ways on how organizations handle and derive insights from their data. Its growing popularity stems from the need for storage systems that not only manage massive, diverse types of datasets but also offer an open architecture and a unified platform for both batch and streaming workloads. As we have noted, data architectures such as data warehouses (OLAP database) and data lakes have each served specific needs—data warehouses excel in structured workloads and provide robust data management, while data lakes are known for supporting diverse data types and are very cost effective. The lakehouse architecture targets the gap between these two, offering an approach that brings out their strengths while addressing their limitations.Lakehouse architecture is built on two foundational pillars: openness and reliability. By storing data in open storage formats, the lakehouse treats storage as an independent and open...

Lakehouse – architectural breakdown

In this section, we’ll break down the key components of a lakehouse architecture and understand how they interface with each other to provide an open foundation for the lakehouse architecture. To begin, let’s first take a look at the lakehouse architecture breakdown:

We will discuss the components of lakehouse architecture in the following sections. The idea is to establish a standard definition for each of these technical components and go over the functionalities and their role in the lakehouse architecture.

Attributes of an open data lakehouse

In this section, we will explore the defining attributes that give open data lakehouse architecture its edge for today’s analytical workloads. These attributes form the foundation of how an open data lakehouse architecture delivers openness, reliability, and cost-efficiency, enabling running multiple analytical workloads on the same architecture. As we explore each attribute, you'll see how they come together to optimize data management, reduce costs, and ensure seamless integration across various workloads and compute engines.

Summary

In this chapter, we explored the evolution of data architectures, from OLTP and OLAP systems to data lakes, providing a foundation for understanding the data lakehouse paradigm. This historical context helps explain how the challenges of older systems led to the development of more flexible and scalable solutions, even as foundational components remained the same. You also gained a deep understanding of the core components and principles of open data lakehouse architecture, including storage, file formats, table formats, storage engines, catalogs, and query engines. These building blocks will help you design scalable and flexible systems capable of handling both batch and streaming workloads efficiently. Additionally, you learned about some of the key attributes of lakehouse architecture, such as open data architecture, modularity, flexibility, and cost-efficiency.In the next chapter, you will dive deep into the transactional layer of the lakehouse to understand how critical technical...

Questions

1. What are the main differences between OLTP and OLAP systems in terms of their primary focus and technical components?
2. Explain the role and characteristics of data lakes in modern data architectures. What limitations do they have that led to the development of the lakehouse architecture?
3. What are the key attributes of a lakehouse architecture that make it distinct from traditional data lakes and warehouses?
4. How do table formats such as Apache Iceberg, Apache Hudi, and Delta Lake enhance the functionality of a lakehouse architecture?
5. What are the benefits of using an open data architecture in a lakehouse setup? Provide examples.
6. Why is unification of batch and streaming workloads an important feature of the lakehouse architecture? How is this achieved?
7. Describe the importance of file formats (row-based and columnar) in a lakehouse. How do they impact query performance and storage efficiency?
8. What role does the storage engine play in a lakehouse architecture, and how does it ensure...

Answers

1. OLTP systems focus on real-time transactional processing, optimized for rapid data insertion, updates, and deletions with ACID compliance. OLAP systems target large-scale data analysis with high query performance, often using column-oriented file formats and denormalized schemas.
2. Data lakes provide centralized storage for raw data in its native format, enabling scalability and cost-effectiveness. However, they face challenges such as lack of governance, slow query performance, and no native ACID support, which the lakehouse architecture addresses by combining the flexibility of data lakes with the reliability of data warehouses.
3. Key attributes of a lakehouse architecture include open data architecture, unification of batch and streaming workloads, cost efficiency, reliable transactions, and multi-compute engine support.
4. Table formats such as Apache Iceberg, Apache Hudi, and Delta Lake provide schema evolution, time travel, and ACID compliance, enabling better data management...