Navigating Data Management: Warehouses, Lakes and Lakehouses

Panorama of modern data management methods: database, data warehouse, data lake, data lake warehouse and data grid

Core points:

• Databases, data warehouses and data lakes have their own advantages in data management. Databases provide structured repositories for efficient storage and retrieval of data; data warehouses are structured repositories specifically used to store, manage and analyze structured data; data lakes can store large amounts of raw data in their native format, including structures , semi-structured or unstructured data.
• Data lake warehouses and data grids are the latest innovations in the field of data management. Data Lake Warehouse combines the versatility of data lakes and the structured processing capabilities of data warehouses to provide a unified storage infrastructure. Data grids take a decentralized approach to treating data as products managed by dedicated teams.
• Organizations do not necessarily replace old data management methods with these new concepts, but use a combination of multiple methods to take advantage of the various technologies. Machine learning tools are increasingly used in data management, and they also enhance the value and operability of data through the introduction of intelligent automation.

In today's dynamic data management environment, terms and concepts related to data storage and processing are becoming increasingly complex. Businesses face the major challenge of effectively handling the surge in data from different sources. This article aims to clarify various data management approaches, provide examples of tools for each concept, and provide a roadmap for a modern data management environment.

Database: Basics

Databases have long been the cornerstone of data management, providing structured repositories for efficient storage, organization and retrieval of data. They can be roughly divided into relational databases and NoSQL databases, each designed for specific data needs and use cases. SQL solutions often involve normalized patterns and meet the needs of OLTP use cases, while some NoSQL databases are good at handling non-standardized data.

The main features of the database include:

• Structured data storage. Databases are good at processing structured data and ensure data integrity through predefined patterns.
• Efficient row-level query. The database is optimized for row queries, and when the query is "correct", the database can retrieve a single or multiple records very quickly by leveraging the index.
• Simplely delete and update. The database can efficiently handle updates or delete single rows.

While databases are very powerful in managing structured data, they may have limitations in handling unstructured or semi-structured data and are not suitable for analytical queries involving readings of millions or billions of rows at a time. This limitation facilitates the development of more specialized solutions such as data warehouses and data lakes, which we will explore in the following sections.

For classic SQL options, PostgreSQL and MySQL are worth paying attention to, while in terms of NoSQL, examples include MongoDB and Cassandra. The term “NoSQL” itself covers databases for different use cases.

Data Warehouse: Structured Insights

Data warehouses are the cornerstone of data management, which act as structured repository designed specifically for storing, managing and analyzing structured data. They perform well in providing powerful performance for analytical queries. A defining feature of a data warehouse is its write-on-time schema method, where data is carefully structured and transformed before loading into the warehouse.

The main features of data warehouse include:

• Structured data. Data warehouses are best suited for structured data such as sales records, financial data and customer information.
• Write mode. Data is carefully structured and transformed before loading into the repository. This ensures data quality and consistency, but also requires developers to write some code when integrating new data sources or existing data sources to change their output.
• Optimized for analysis. Data warehouses are designed to enable fast query performance, making them ideal for business intelligence and reporting.

Despite the advantages of data warehouses, there are limitations in handling unstructured or semi-structured data as well as real-time data processing.

Some notable examples include Snowflake, Amazon Redshift, and Apache Hive.

Data Lake: Unlimited Possibilities

As businesses work hard to process larger quantities and different types of data from multiple sources, data lakes have become a complementary solution. A data lake is a repository that can store large amounts of raw data in its native format, whether structured, semi-structured or unstructured.

The main features of the data lake include:

• Raw data storage. Data lakes typically store data in their original form, making it suitable for various data types. It can be either a table exported from a relational database, a plain text log collected from multiple systems, or even binary data such as images.
• Read-time mode. Data is structured and transformed when read, allowing for flexibility in data exploration and analysis.
• Scalability. Data lakes can be very easy to scale horizontally to accommodate almost any amount of data.

While data lakes are good at storing big data, they can become difficult to manage without proper governance and data cataloging and become the infamous “data swamp.” Typical definitions of data lakes do not include utilities for data management, governance, or query. Some companies enhance these features by introducing the concept of "data lake warehouse".

Data Lake Warehouse: The best of both worlds

Data Lake Warehouse marks the latest innovation in the field of data management, aiming to bridge the gap between the versatility of data lakes and the structured processing capabilities of data warehouses. They unify both worlds by providing a unified and organized storage infrastructure for structured and semi-structured data while supporting efficient analytical processing. Data Lake Warehouse supports traditional "warehouse-style" analysis and query built on top of data lakes.

The main features of the data lake warehouse include:

• is still scalable. Since data lake warehouses are built on top of data lakes, they still allow for high scalability and storage of data in different formats.
• Mode evolution. They allow patterns to evolve so that data can be ingested in their original form and structured when needed.
• A ready analysis. Data Lake Warehouse provides the functionality to perform queries and data indexes, similar to data warehouses.

Popular examples of data lake warehouse systems include Delta Lake (provided by Databricks), an open source storage layer that provides ACID transactions and schema enforcement for data lakes, and Iceberg, an efficient focus on data lakes. Open source projects in transactional table formats that provide the same ease of use and reliability as data warehouses.

Data Lake Warehouse is gaining attention as businesses aim to simplify their data architecture, reduce data silos and enable real-time analytics while maintaining data governance. They represent a promising evolution in the ever-changing data storage and processing environment, addressing the challenges posed by the diverse and dynamic nature of modern data.

Data Grid: Data is Product

The concept of data grid proposes a new perspective on data, defining it as a product managed by a dedicated team, responsible for its quality, uptime, and more. This product-oriented approach can take many forms, from carefully planned data sets to APIs, where business units within the company can independently access and utilize these data products.

Data grid represents a paradigm shift in data architecture, solving the challenges posed by increasingly complex and large-scale data in large organizations. It introduces a decentralized approach to data management, unlike the traditional data warehouse model.

The main principles of data grid include:

• Domain-oriented ownership. Data is owned and managed by cross-functional domain teams that are responsible for data quality, governance, and access.
• Data is the product. Data is considered a product with clear ownership, documentation and a service level agreement (SLA) for data consumers.
• Self-service data platform. Since the team is responsible for providing access to its data, this does not mean that data engineers are unnecessary. They need to create a platform that enables teams to easily share and discover the data they need.
• Joint calculation. Data processing and analysis can now be performed near the data residency location, reducing data movement and improving performance.

Although data grids have received attention in the data management community for their ability to solve decentralization and democratization challenges in large organizations, it may not be suitable for everyone. Small companies may find it more practical to choose a dedicated storage solution that is easier to set up and manage.

Combination method

While I'm trying to outline some kind of "timeline" with the emergence of new tools and concepts, it must be noted that the old methods have not been outdated or replaced. Organizations are adopting multiple approaches to leverage the advantages of various technologies while mitigating potential shortcomings.

One aspect that is not covered in this article is the increasing application of machine learning (ML) tools in data management. These tools automate tasks such as data cleaning, quality monitoring, anomaly detection and predictive analysis. This trend enhances the value and operability of data by introducing intelligent automation into the data management environment.

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