Hey everyone, welcome to today's session from chaos to clarity data lakehouse architectures. So before we dive in, let me ask how many of you have worked with data lakes or data warehouses and how many of you have faced challenges with scalability, cost, governance or analytic performance. So you're not alone. enterprises are generating more data than ever before. So think about this every day companies produce petabytes of data from customer transactions to mission logs by 2025 Global data is expected to exceed 180 zettabytes. That's more than double today's volume So what's the problem here? The traditional systems just can't handle this explosion efficiently So how do we turn this chaos into clarity? That's where the data lakehouse architecture comes in, which combines the best of data lakes and data warehouses into a single scalable platform. So in this session, I will walk you through why traditional systems struggle, how lakehouses solve this issue, and what makes them the future of enterprise data management. So let's get started. So imagine a large retailer processing millions of sales transactions daily. So their data includes structured purchase records, semi structured weblogs, and unstructured customer reviews. If they use a traditional warehouse, they must constantly restructure data before analysis, which might delay insights and increasing costs. So in the similar way, billions of online transactions, supply chain updates across global warehouses, customer behavior data from clicks and purchases, unstructured data from chatbots, emails, and customer support. So all this adds up to petabytes of data every single day. And guess what? It's only increasing. So data is expected to grow 2x every two years. So what challenges we are running into this, right? The number one is the scalability issues. Most legacy systems just aren't built for today's data explosion. As data grows exponentially, traditional databases slow down. Struggle with query performance and become expensive to maintain. And the second challenge is the structure problems. Data lakes are meant to store raw data, but without proper governance, they turn into data swamps, which are unmanageable, disorganized, and hard to query. And the third challenge is the analytics. So traditional data warehouses require expensive infrastructure and rigid schemas, making it difficult to adapt to new business needs. They are great for structured data, but struggle with semi structured and unstructured formats. So at the end, like business needs a system that is scalable, flexible, cost effective and optimized for analytics. That's where the data lake house come in. So what is data lake house? It's a modern data architecture that combines the strength of both data lakes and data warehouses. So let's take a closer look at the components that make lake houses work. The first is the storage layer. Optimized with open formats like Apache Iceberg and Delta Lake for scalability and efficiency. Secondly, the metadata layer. Ensures schema evolution, indexing, and cataloging, making data easier to discover and query. Third, the processing layer. Uses Apache Spark and SQL engines to enable real time and batch processing. And lastly, the governance and security. Supports role based access, encryption, and audit logging for compliance. So by integrating all these layers, lake houses solve the problems of both lakes and warehouses, making data management faster, smarter, and more cost efficient. So what do we give the strengths of both data lake and warehouses, right? From data lakes, you get the scalability, cost efficiency, and support of all data types, right? Like the structured, semi structured, and unstructured. From data warehouses, you get performance, asset compliance, schema enforcement, and optimized querying. Think of a lake house like a well organized library. A traditional data lake is like a warehouse full of random books, messy and hard to find anything. A data warehouse is like a neatly organized section, but limited in space and expense to maintain. So a lake house, it's the best of both worlds, organized, scalable, and efficient. So why does this matter? Enterprises can scale from terabytes to exabytes while maintaining performance, unified storage and compute layers, eliminate unnecessary data duplication. So support for AML workloads without additional data transformations. So cost is a big deal. When managing enterprise data, lake houses help organizations reduce storage and compute costs by up to 30%. But how right scalability like scale from terabytes to exabytes without losing performance essential for aml and real time analytics Second the optimization 30 cost savings through smart data management techniques reducing unnecessary data movements And thirdly governance maintain data quality and compliance while keeping costs low Data remains structured and accessible. So the scalability, efficiency, and governance, three pillars that make lake houses, the go to choice for modern enterprises, for instance, a financial service company saved millions annually by replacing their costly traditional warehouse with a lake house. Reducing redundant storage and processing costs by optimizing tied storage. So a major concern with data lakes is that they lack ACID transactions, which means data consistency isn't guaranteed. So which the lake houses will solve this issue, right? With Atomosity, ensures transactions are all or nothing, preventing partial updates that can corrupt data. Consistency, data remains in a valid state before and after updates. Isolation, concurrent transactions don't interfere, maintaining accuracy, durability. Once committed, data stays permanent, even if the system crashes. so why does this matter, for enterprises, right? Enterprises can ensure data reliability even at scale, like supports critical applications like fraud detection and real time financial transactions. Now, for an example, think about an e commerce company processing thousands of orders per second. Asset Compliance ensures that even in case of a failure, orders are never lost or duplicated. Data is constantly evolving, new sources, new business requirements, new regulatory needs. Traditional warehouses struggle with schema changes, but Lakehouses handle them seamlessly. Firstly, we plan, assess current schema needs and anticipate future changes. Second, implement. Deploy schema updates without downtime. Third, validate. Ensure data consistency and query compatibility. Fourth, monitor. Track performance and adapt dynamically. this flexibility allows organizations to integrate new data sources without disrupting workflows. Moving to the next slide. Lakehouses aren't just about storage, right? they enable real time analytics using Apache Spark. For instance, with the speed, process petabytes of data with sub second latency for real time decisions. Secondly, flexibility. Runs both BI dashboards and ML models on the same infrastructure. Unified platform. Handles streaming, batch processing, and interactive queries efficiently. So whether it's fraud detection, recommendation engines, or supply chain optimization, Lakehouse delivers insight in real time. For example, a ride sharing company uses lake houses to analyze driver locations, ride requests, and pricing data in real time, ensuring dynamic fare adjustments within seconds. So traditional ETL process is expensive and slow. Lakehouse has solved this by enabling in place data processing. First, with the ingest, direct ingestion from multiple sources without pre processing. Data transformations happen directly within the Lakehouse using SQL and Python. Third, with the low immediate data availability for analytics. So this reduces infrastructure costs, eliminates unnecessary data movement, and accelerates insights. As an example, a healthcare company reduced patient data processing time from 24 hours to just 30 minutes by eliminating redundant ETL steps in their lake house. So in most enterprises, data is locked in silos. Different departments struggle to access relevant information. Lake houses solve this by creating a single source of truth. By, with secure role based access to all data sources. Powerful search and metadata tools to find the right data instantly. Seamless collaboration across departments. So by democratizing access, businesses boost efficiency and innovation. So at the end of the day, data is only as valuable as the insights it delivers. So with the Lakehouse infrastructure and architecture, 30 percent cost reduction is expected through optimized storage and processing. And 90 percent faster queries, thanks to the advanced indexing and caching. So from predictive analytics to AI driven insights, lakehouses help business make smarter, faster decisions. All right, so let's recap. Here's why lakehouses are game changers. Lakehouses scale from terabytes to exabytes while keeping performance high. They eliminate ETL inefficiencies and enable real time analytics. They ensure asset compliance and strong governance for enterprise grade reliability. So they reduce infrastructure costs by 30 percent while driving AI driven insights. So they bring governance, compliance, and security to enterprise data. So in short, lake houses provide the clarity, efficiency, and innovation Enterprises need to thrive in data driven world. So the future of data is here and it's built on lake houses. So thank you for joining me. Let's keep pushing the boundaries of what's possible with data. I hope this session has given you new insights on how to optimize your enterprise data strategy. Thanks again. And I'll see you next time.