What is Databricks Lakehouse and why you should care
In recent times, Databricks has created lots of buzz in the industry. Databricks lays out the strong foundation of Data engineering, AI & ML, and streaming capabilities under one umbrella. Databricks Lakehouse is essential for a large enterprise that wants to simplify the data estate without vendor lock-in. In this blog, we will learn what Databricks Lakehouse is and why it is important to understand this advanced platform if you want to streamline your data engineering and AI workloads.
I have created a YouTube video for this topic.
Data & AI Maturity Curve
For any large enterprise, its competitive advantage depends upon how mature they are in data and AI. The more mature they are, the more successful they become. This gives them a competitive advantage against their competitors. The data and AI maturity journey have these phases.
- Clean Data: Data and AI journey starts with collecting the data from various sources and then cleaning it so that it can be more useful.
- Generating the Reports: Once the data is cleaned it can be used to generate useful reports to generate business insights.
- Ad-Hoc Queries: Once the reports are created, then business users run ad-hoc queries on these reports so that they can gain deeper insights into data.
- Data Exploration: At this stage, the enterprise is fully capable to run insights and explore the data further by slicing and dicing the data.
- Predictive Modelling: Once you have the data from previous years we can create AI models to predict future trends. Predictive modeling uses statistics to predict outcomes. Most often the event one wants to predict is in the future.
- Predictive Analytics: At this stage, the use of data, statistical algorithms, and machine learning techniques are used to identify the likelihood of future outcomes based on historical data. This gives a competitive edge to the enterprises.
- Automated Decision making: Automated decision-making helps organizations to take the right decision at right time with the help of AI. And those decisions are best in the industry. Every organization wants to reach this peak level in the data maturity curve.
If we see carefully stages 1 to 4 are looking in flashback to understand what happened in the past. These are BI use cases and for these, we use a data warehouse. The Data warehouse stores the data from previous years to generate insights.
Stages 4 to 7 help us to predict the future and help us understand how we will behave in the future based on business constraints and how we can react in real-time. when companies become more mature in taking automated decisions they earn a competitive advantage and their business grew exponentially. Every company strives to reach this stage.
What are the challenges of handling Data & AI use cases together?
In order to implement BI and AI use cases together companies try to ingest their data into the data lake (to cover AI use cases) and then ingest this data into the data warehouse(for BI use cases) as depicted below.
Now keeping the same data in two different systems has its own challenges:
- Data is duplicated in two different systems so you end up paying twice for the same data one for the Data warehouse and another for the data lake.
- It becomes difficult to sync changes between two systems. If one change happens in the Data warehouse you need to immediately propagate the change into Data Lake and vice versa.
- Data drift is caused by unexpected and undocumented changes to the data structure and semantics and it can break processes and corrupt data.
- Now you need two sets of experts one who knows data warehouse and another who is an expert in AI use cases because the tool stack for AI and BI is completely different.
- The problem multiplies when there is no collaboration happens between the support teams responsible to support these two platforms. They do not know what changes are made by other teams and how it is going to impact them.
- BI use cases are not addressed by AI systems and vice versa which makes these systems incomplete support for each other’s use cases.
- Security and governance models work differently for BI and AI systems and it becomes a potential bottleneck to maintain the same set of security simultaneously for these systems because they are essentially two different technology stacks.
- Copying data from a Data lake to a data warehouse and vice versa creates duplicate data which lives in silos and is more costly.
The disadvantage of two disparate, incompatible data platforms
There are many disadvantages to keeping data in two disparate incompatible data platforms.
- Too complex, Expensive, and rework is required when any change happens in one system.
- Time and resources are wasted to bring the right data to the right people at right time.
- Impossible to achieve the full potential of Data, Analytics, and AI with this approach.
- Unable to compete due to the slowness of data being available when it moves between systems.
Need for a solution that can bridge this gap
To resolve the above problems we need to find a solution which can address these issues. The system should have these characteristics:
- Rather than copying and transforming data in multiple systems, it should store the data in one platform.
- We need ONE platform that can accommodate all data types.
- We need an Open standard-based platform so no vendor lock-in and we can change the vendor anytime. This is most important because AI-based code is mostly written in open-source Python libraries.
- We need ONE security and governance model which is cloud-agnostic to govern data wherever it’s stored and supports multi-cloud platforms. So we are not tied to one cloud platform at the same time governance mode should be easy to apply to the entire platform in one shot.
The databricks lakehouse exactly does the same:
- It unifies data warehousing and AI use cases on a single platform
- Built on open source and open standards
- It is available on all major clouds (Azure, GCP, and AWS)
Advantage of Lakehouse
One platform Best of both worlds (BI & AI)
- All structured, semi-structured, and unstructured data are in one place.
- It provides fast and flexible data processing capabilities, including support for real-time streaming, batch processing, and interactive querying.
- Reliable and extremely fast query performance without leaving the data lake with an open technology (called Delta Lake) So that you can use it for data warehousing workloads and AI workloads simultaneously.
- It integrates with a variety of popular data analytics and machine learning tools, making it easy to perform advanced data analytics and build machine learning models.
- It is scalable and highly available, so you can easily handle large volumes of data and ensure that your data is always available when you need it.
- Unity Catalog provides governance for all the data with one consistent model.
- Databricks tools allowed everyone to collaborate on the same data. Two or more developers can coauthor the Databricks notebook.
How it simplifies your data Architecture?
- It simplifies your data architecture because it can handle all workloads (Data lake, Data warehouse, and streaming use cases). Which means data warehousing, data science, ML, data engineering, and data streaming in ONE platform.
- The entire platform is built on open source and open standards. This means you have a lot of freedom in how you evolve your data strategy.
- Vendor Lock-in restricts present and future choices but databricks is an open-source platform and does not restrict you.
- A vast community of talent resides in the open-source community which can reduce the cost of managing this platform.
- It is designed to be easy to use, with a user-friendly interface and a variety of tools and resources to help you get started quickly.
- Finally, you get one consistent experience across all clouds due to multi-cloud and you don’t need to invest in reinventing the wheel for every cloud platform that you’re using to support your data and AI efforts.
Data warehouse support for external tables and its pitfalls
Some of the data warehouses provide a concept called external tables which is a read-only copy of data residing in the data lake but there are lots of issues with this approach and we can not say it is true lakehouse Architecture:
‘Read-Only’ External Table Support from the data lake means it is one-way access to the data i.e. which means the data you are accessing is read-only and you can not write to it. Data warehouses use vendor-specific technology and this can not be truly open source Lakehouse approach.
Overall, Databricks Lakehouse can be a powerful tool for organizations looking to store, process, and analyze large volumes of data at scale. It can help you gain insights from your data, improve your decision-making, and drive business growth. It helps to reach the peak of data maturity.
+ There are no commentsAdd yours