Data is the quintessential ingredient of a successful business recipe. How you treat your data determines your sustainable business growth. Data needs a place to live, organize, analyze, and convert into insights.
Organizations across industries have been relying on Data warehouses for several years to handle their data. With the proliferation of a massive amount of operational data, organizations envisioned a single system to host a large amount of data for different analytics workloads. That single system that every data architecture is relying on is the Data Lakes.
While the data lakes succeeded the legacy architectures with an edge over the critical competencies, they failed to match the changing demands of the businesses in the areas of integrations, consistency, data quality, machine learning algorithms. But here goes the saying, every problem comes with a solution. These limitations laid a foundation for a flexible and cost-effective data management architecture called Data Lakehouse.
So this pops a question in a different dimension. Are the previous data models obsolete or a failure? To answer this question, we first need to understand data Lakehouse, its emergence and the purpose it serves.
Data Lakehouse: Data Lakehouse is an open data architecture that includes the best of data lake and data warehouse components. It addresses the problems and limitations with the previous data architecture models – Data lakes and Data warehouses.
“A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.”DataBricks.
Data Lakehouse is the combination of these two data models – Data Lakes and Data Warehouses. The previous models are not dead, but they are combined into one architecture while addressing these two models’ limitations and offering higher efficiency at a low cost.
Here are some of the challenges with the current data models
Lack of open formats – It takes enormous efforts, time, and cost to migrate data to other systems from the data warehouse owing to the complexity of storing data into exclusive trademarked formats only.
Lack of machine learning support – Several research and studies were done to get ML and data management work smoothly, but none of the pioneered ML systems delivered exceptional results.
Higher Cost – Storing data in Data Lakes and Data Warehouses seemed to be costly for the organizations.
The Emergence Of Data Lakehouse – Inventions are the outcome of the necessity. The current data architectures have limitations which cause problem to the team members. To eliminate these limitations, Data Lakehouse is emerged. The past models were demanded for too much effort, cost, and, most importantly, time. These limitations prevent the leaders to get prompt real-time insights.
Can you imagine how much data we generate every day?
Over 2.5 quintillion bytes of data are created every single day – (Domo)
Yes, with this enormous volume of data comes the challenge to extract, transfer and load the data promptly.
5 Reasons To Move Your Data to Data Lakehouse
There are many reasons to move your current data architecture to data Lakehouse as every organization has its own challenges in creating and managing a sustainable data architecture. We have curated the top 5 reasons why you should consider storing data in data lake house:
- Less Time and Effort Administrating – Time is money in business decision-making, and the estimated decision-making success on accurate real-time data and insights is considerably high. Team members can save time and effort by integrating the data Lakehouse architecture. Besides, it requires minimum effort and less time in storing, processing, and delivering insights. A single platform would ease the administrative burden on a larger scale.
- Simplified schema and data governance – One of the biggest concerns of the tech team is managing the Data governance on various tools. With the help of Data Lakehouse, teams can remove the operational overhead of managing Data governance. While transferring sensitive data from one tool to another, you need to be extra cautious to ensure that each tool maintains the access controls and encryption properly. However, if you integrate the Data Lakehouse program, you can manage the data governance from one source. Having all data pipelines under one roof will simplify data governance and schema management. Data Lakehouse architecture is built on the principle of putting everything under a roof to make data storage, data governance, and schema management easy.
- Reduced data movement and redundancy – While using the data Warehousing method, you have to load the data into the data warehouse to perform analysis or query. For example, loading the data into the Data warehouse from our existing data lake by performing the cleaning task and transferring the data into the destination schema with the help of ETL tools. The Data Lakehouse tool helps teams to eliminate the ETL process by connecting the query engine to the Data lake. Due to Data redundancies teams cannot create one-point source of truth. Having a data warehouse and multiple lakes will lead to inefficient data movement, causing more redundancies. With Data Lakehouse you get benefits of less data redundancy and data movement.
- Direct access to data for analysis tools – Data Lakehouse enabling tools Apache Drill tool, which enables Data Lakehouse supports the connection with some of the most-sought after BI tools such as Tableau and PowerBI. This feature eliminates the time taken to convert raw data into reports. Why would you run back and forth to several tools and platforms to get real-time and batch analytics done when you can get it under one platform? Yes, Data Lakehouse enables you to have real-time and batch analytics under one platform.
- Cost-effective data storage – The tech team used to store data in various places in the Warehousing Data Lake method. The data storage cost was also very high. Comparatively, Data Lakehouse offers cheap Data storage options such as Blob, S3, etc. Managing multiple systems (warehouses, lakes, and other tools) would be a costly and time-consuming affair. A single solution for a multiple-systems solution, Data Lakehouse.
Data Lakehouse gives you features of these two architectures under one roof. Data Lakehouse is pretty much easy to install, and use compared to previous data architecture. Organizations having less volume of data can also get advantages by integrating this architecture in their business. Organizations found a solution for the limitations they have been facing with the previous architectures. With the increasing data volume, we can expect the scope of improvements. We need faster insights more than ever. Would Data Lakehouse be able to survive the enormous volume of data?
Is Data Lakehouse the latest paradigm shift, or is something more innovative coming? We have heard about Data Mesh, a more decentralized data architecture platform. We will discuss data mesh in detail in our next blog.
Here is the podcast episode link of our podcast series, The Data Story. This episode was about Data Lakehouse: Debunking the hype. Listen to the conversation between industry veterans James Serra and Khalil Sheikh.
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