Understanding the Lakehouse Paradigm with Midoffice Data: Part 1 – The Evolution of Data Storage & Processing

Data is the lifeblood of innovation in today’s fast paced digital highway. It has evolved over the years to meet the growing business demand and technology innovations. The initial asks were about reactive reporting that led the path for datawarehouse evolution. With the advent of web, new age information and AI, datalakes took the centerstage to manage the polyglot data types. The current need of a centralized datawarehouse that can give the benefits of both a data lake and datawarehouse takes centrestage as the gaps between business intelligence and AI morphs into augmented intelligence and the need for a connected data ecosystem. The Lakehouse marks a significant milestone in the journey of finding a superior data management practice. Let us trace this journey in detail to understand why a lakehouse has become a force to reckon with in the enterprise world. 

Era 1: Traditional Datawarehouse

Before the advent of cloud storage and big data, businesses primarily depended on data warehouses. The concept of the traditional datawarehouse is widely attributed to Bill Inmon and he is also known as the “father of the datawarehouse”. The needs of this era were straightforward – build an optimized data storage that can meet enterprise reporting needs aka analytics. Advanced analytics such as Machine learning and unstructured data were not a norm in the enterprise. These Enterprise Datawarehouses were optimized for storage and structured for reporting needs – a single point of repository for harnessing the data for BI applications and consistent query performance. However, these datawarehouses were

• not flexible as they needed large data centers.
• cost prohibitive as data volumes grew because cost of storage was very high
• unable to support semi-structured and unstructured data, and
• fit for reactive reporting.

The datawarehouses were also very costly to build with infrastructure costs running into millions of dollars that included licenses for servers and the ETL tools. Availability of talent was also limited that made it a really long timeframe to deploy a datawarehouse. A new wave of data management was required to overcome these challenges and also incorporate the innovation in compute and storage costs, artificial intelligence and cloud computing.

Era 2: Data Lakes

The concept of Data lakes started with the arrival of Hadoop. Hadoop was created by Doug Cutting and Mike Cafarella who were solving for the Nutch search engine at Yahoo. Hadoop was based on a concept of divide and conquer. The term “Data lake” was coined by James Dixon, former CTO of Pentaho, to describe a large repository of raw data held in its native format. Google BigQuery and Yahoo were one of the first few companies who introduced the concept The concept followed 3 basic principles:

• Distribute data into multiple files and distribute the files across multiple nodes
• Process each of these files locally at each node
• Use an orchestrator that communicates each node and aggregate the file once the processing is completed.

In contrast to the structured world of data

• Ability to process large volumes of data
• Agility that enabled fast-changing business requirements

These advantages enabled data lakes to store enormous volumes of raw data, regardless of the structure or format. This made them ideal for businesses dealing with diverse data types from web pages to IoT sensors. However, it also presented its own challenges:

• Without a structure, the data lakes turned into data swamp making it challenging to retrieve or process meaning
• Lack of data governance and enforced schema meant that data quality could easily degrade over time.
• Lack of talent availability that created a vacuum in enterprise implementation and support complexities

Era 3: Data Lakehouse

Recognizing the strengths and weaknesses of bothigence (BI) and machine learning (ML) on all data.

Lakehouse are becoming important because they enable enterprises to break silos between data lakes and data warehouses, and to manage all of their data in a single, unified repository. The adoption of Lakehouse architecture brings with it a host of advantages, key among them are:

• Enhanced data governance and security: A centralized location simplifies the enforcement of governance policies and security measures, ensuring better control and protection of the data assets. This is important because unlike applications, a data repository contains the enterprise data and not just a single application’s data.

• Cost efficiency: Lakehouse architecture reduces the need for separate data management systems thereby lowering the storage and compute expenses.
• Boosted Agility and Innovation: Lakehouses enables organizations to swiftly access and analyze their entire data spectrum leading to faster decision making and increased innovation.
• Scalability: Lakehouses can be scaled horizontally to meet the needs of largest organizations.
• Talent: With lakehouse, enterprises can quickly re-skill their existing talent to Lakehouse architecture bringing in the best of software engineering and data engineering practices. 

The above advantages bodes well for Lakehouses making it suitable for variety of use-cases – Business Intelligence, machine learning, data warehousing, data science and application development. With the availability of the open source lakehouse tools, a lakehouse architecture can be deployed both on-prem as well as on cloud. Some of the best Lakehouse platforms available are Databricks, Delta Lake, Snowflake, Apache Hudi, Google BigQuery, Azure Synapse, AWS Redshift, Apache Iceberg.

As we advance in our series, we’ll delve deeper into the architectural nuances of the Lakehouse and understand its significance in modern data management. Stay tuned for our next blog, where we dissect the technical backbone of the Lakehouse.

For businesses looking to stay ahead in the data game, embracing evolving paradigms like the Lakehouse becomes imperative. At Midoffice Data, we are committed to facilitating this evolution and hence have built our astRai solution as a data-first stack on Lakehouse architecture, ensuring our clients remain at the forefront of data-driven innovation.

Stay tuned for Part 2 of this series. Meanwhile, if you are interested to learn more about how astRai can play a pivotal part of your data strategy, reach out to us.

 

Reference: What is a Data Lakehouse? (databricks.com)

Data Lakehouse in Action by Pradeep Menon

https://www.eckerson.com/articles/data-architecture-complex-vs-complicated