From my last post, we’ve been exploring the intricate world of data analytics and engineering.

Today, let’s dive into the essential data storage systems crucial for effective data management. At KareTech, selecting the right system is paramount, tailored to our client needs. This article unpacks the fundamentals, characteristics, advantages, and limitations of various data storage systems.

Databases? What are they?

Databases, the data management backbone, serve as organized playgrounds where information resides in tables with rows and columns. A popular query language known as SQL is used to manipulate the data in the tables. However, not all databases are the same; there are

1. Relational databases
2. Flexible NoSQL databases
3. Lake databases are a combination of the best of both worlds.

At KareTech, we specialize in utilizing numerous cloud-based (vendor) database management systems (RDBMS) based on a use-case basis for our clients.

THE ACID TEST

The ACID test, ensuring Atomicity, Consistency, Isolation, and Durability in database transactions, forms a crucial framework for data integrity and reliability.

In database transactions, ACID stands for Atomicity, Consistency, Isolation, and Durability.

1. Atomicity: Ensures that a transaction is treated as a single, indivisible unit of work. Either all operations within the transaction are completed, or none of them are.

2. Consistency: Guarantees that a transaction brings the database from one valid state to another, adhering to predefined integrity constraints throughout the entire transaction.

3. Isolation: Ensures that multiple transactions can occur concurrently without interfering with each other, maintaining independence and preventing interference.

4. Durability: Guarantees that once a transaction is committed, its effects are permanent and survive any subsequent system failures. The changes made by committed transactions persist even during power outages or crashes.

Challenges

However, databases have their challenges, such as rigidity and scalability issues, particularly with unstructured data. Scaling databases can be complex and costly.

…and there are Data warehouses!

Shifting our focus to data warehousing

These centralized systems consolidate data from diverse sources to facilitate efficient complex querying and analytical processing. While data warehouses share the fundamental concept of a database system, they are uniquely structured to cater specifically to machine learning and reporting requirements. Essentially, these warehouses serve as repositories for historical data, extensively utilized by major corporations for reporting purposes.

For instance, when a company aims to compute its sales figures, these historical datasets become instrumental in generating dashboards that offer insights into the business’s performance.

Finally, they excel in Online Analytical Processing (OLAP), supporting tasks like data mining and trend analysis.

Types

Data warehouses come in types like

1. Enterprise Data Warehouse (EDW): A centralized system (On-Prem) that integrates and stores comprehensive data from various sources within an organization, providing a unified platform for analysis, reporting, and business intelligence to support decision-making processes.
2. Data Mart: Data marts are organized around specific business subjects or areas, such as sales, marketing, finance, or human resources.
3. Cloud-Based Data Warehouse: Hosted on cloud computing infrastructure such as Azure or AWS, allowing organizations to store, manage, and analyze large volumes of data without the need to invest in and maintain on-premises hardware and infrastructure.

Challenges

Yet, data warehouses face challenges with unstructured data and scalability. Handling diverse sources demands careful consideration of data quality and consistency. Hardware, software, and maintenance costs need meticulous management. From a developer stand point, running complex ETL processes sometimes takes time! In upcoming articles, we’ll explore ETL methodologies and other aspects.

Emergence of modern storage system

Data lakes have emerged as a game-changer in the world of data management, offering unparalleled flexibility and scalability. However, as organizations dive into the vastness of data lakes, they must navigate challenges to ensure their potential is harnessed effectively.

What are Data Lakes?

Data lakes serve as centralized repositories for diverse data types, accommodating both structured and unstructured data. They are highly scalable and capable of handling large volumes of data from various sources. Data lakes are versatile storage solutions suitable for a wide range of applications, from big data analytics to machine learning and IoT. They provide a holistic view of the data landscape, breaking down silos and enabling cross-functional insights.

We will talk about how to build an enterprise data lake properly in our next article in this series.

Challenges

Data Lakes provides a versatile storage solution, accommodating structured and unstructured data. Highly scalable and suitable for various applications, however, data lakes lack ACID compliance, making them vulnerable to data swamps without proper governance. Due to their unstructured nature, they are not ideal for reporting and Business Intelligence (BI) analytics.

…..now my favourite part!

Bridging the Gap Between Warehouses and Lakes

Let’s paint a picture, imagine if data lakes & data warehouse were to have kids! What do we get? The result of that is a data lakehouse! 🙂

The data lakehouse emerges as a unified platform, combining the strengths of data warehouses and data lakes. This innovative solution offers large-scale data storage in an unstructured format while maintaining ACID properties, making it capable of handling diverse data formats. Data lakehouses support various analytical workloads and boast improved data governance. Companies can leverage data lakehouses for machine learning, reporting, and analytics needs.

As organizations seek a unified and versatile solution for their data management needs, the emergence of the data lakehouse is reshaping the landscape.

This innovative platform brings together the structured approach of data warehouses and the flexibility of data lakes, offering a comprehensive solution for large-scale data storage and analytics.

…….Some of the Key Benefits

1. ACID transaction support for data consistency.
2. Schema enforcement and governance ensuring data integrity and auditing.
3. Robust data governance.
4. BI support, reducing latency in deriving insights.
5. Decoupled storage from compute, enabling independent scaling of components.
6. Open storage formats like Parquet.
7. Support for diverse data types and workloads.
8. End-to-end streaming for real-time reports.
9. Scalable handling of both data and metadata.
10. Audit history and time travel capabilities.
11. Schema enforcement and evolution.
12. Support for deletes, updates, and merges, including complex use cases like change data capture (CDC) and streaming upserts.
13. Unified streaming and batch data processing.
14. Transaction log ensuring users work with the most up-to-date information.

In essence, Delta Lake represents the modernized iteration of a data warehouse, offering all the benefits and features without compromising the flexibility inherent in a data lake.

It stands as a testament to the evolution in data management, addressing the challenges faced by its predecessors while ushering in a new era of efficiency, governance, and scalability.

So there you have it!

Choosing databases, data warehouses, data lakes, or data lakehouses depends on specific analytics needs and use cases as we said earlier in this article. Each system has unique strengths and limitations, making it essential for organizations to assess their requirements before making a choice.

Finally!

Understanding the fundamentals of database systems is crucial for making informed decisions in data management. Whether opting for the structure of databases, the analytical power of data warehouses, the versatility of data lakes, or the unified benefits of data lakehouses, organizations must align their choices with their analytics needs to unlock the full potential of their data.