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What is Data Lake, Warehouse ,Lakehouse ?

What is Datalake, warehouse, datalake

Introduction

Data Lake: A data lake is a storage system that allows organizations to store vast amounts of raw, unstructured, semi-structured, or structured data in its native format. It provides flexibility by not requiring predefined schemas and can handle large volumes of data, making it ideal for big data and machine learning use cases.

Data Warehouse: A data warehouse is a centralized repository designed to store structured data from multiple sources. It uses predefined schemas and is optimized for analytical queries, reporting, and business intelligence. Data in a warehouse is typically transformed, cleaned, and loaded through ETL (Extract, Transform, Load) processes.

Lakehouse: A lakehouse combines the best features of data lakes and data warehouses. It stores raw data like a data lake but adds management features such as ACID transactions, schema enforcement, and data versioning, making it suitable for both analytics and machine learning workloads. The lakehouse aims to offer the scalability of a lake and the structure of a warehouse.

(Relational) Data Warehouse

Imagine starting a job at a company where you’re responsible for creating reports and visualizations for the business team based on the data from the company’s product. Initially, it’s simple—there’s just one database that records transactional data, and you pull data directly from it to generate reports.

Soon, the company starts using a third-party service, and the business users request data from this service to be included in their reports. You can still manage this by pulling data from both the database and the third-party API, joining and aggregating it to create the reports.

But as the company grows and adds more services and integrates with additional external tools, each generating its own data, your end users want all of this data included in their reports. At this point, pulling data from each source separately and manually combining it becomes unmanageable.

This is where the data warehouse comes in. It acts as a centralized repository where large amounts of data from various sources can be stored and managed, serving the company’s analytics needs.

Initially, data is extracted from multiple sources, transformed into a predefined structure (schema-on-read), and loaded into the data warehouse. This setup helps businesses centralize their data, improving data management and enabling more efficient analysis.

 

However, challenges soon arise. Data doesn’t always come in a structured tabular format—there’s also unstructured data such as videos, audio, and text documents. This unstructured data creates significant challenges for relational data warehouses, which are designed to handle only structured data.

Data Lake + Data Warehouse

What is data lake & warehouse

You’ve likely heard the term Big Data during your career.

Big tech companies that survived the DotCom bubble in the early 2000s, such as Yahoo, Google, and Amazon, were pioneers in working with Big Data. Initially, they continued to rely on traditional data warehouses for centralizing their data. However, these systems struggled to scale with the rapidly increasing volume and variety of data.

To address this, Yahoo developed Apache Hadoop, which included processing (MapReduce) and storage (HDFS) based on Google’s papers on MapReduce and the Google File System.

Data at this point was no longer strictly structured; unstructured data—such as videos, text, and documents—was recognized as valuable for generating business insights. However, relational data warehouses were only capable of managing structured data, creating a gap.

This led to the introduction of the data lake.
A data lake allows vast amounts of raw data to be stored in its native format (such as in HDFS or later, in cloud object storage). Unlike traditional data warehouses, the data lake doesn’t require a predefined schema, allowing all types of data, including unstructured, to be stored without format constraints.

Initially, there was an attempt to replace the traditional data warehouse with the data lake by bringing processing directly on top of it. However, this approach had several issues, and the data lake eventually became a “data swamp” due to a lack of key data management features, such as data discovery, quality assurance, ACID transactions, and support for data manipulation.

Therefore, combining the strengths of both the data lake and the data warehouse became the optimal solution.

Data Lakehouse

Data Lakehouse

Maintaining the two-tier architecture of a data lake and data warehouse comes with several challenges:

  • Data Staleness: The data in the warehouse can be outdated compared to the more recent data in the lake. This creates a drawback from the traditional data warehouse setup, where new operational data is available immediately for analytics.
  • Reliability: Consolidating data between the lake and the warehouse is difficult and costly. It requires substantial engineering effort to ETL (Extract, Transform, Load) data between the two systems.
  • Limited Support for Advanced Analytics: Data warehouses initially struggled with supporting machine learning workloads, as these tasks require processing large datasets with complex code. Users often exported data to files, increasing complexity. Alternatively, running workloads directly on data in the lake came at the cost of losing features like ACID transactions and data versioning, though modern data warehouses like BigQuery now offer better support for machine learning within their environments.
  • Total Cost of Ownership: Data duplication in both the lake and warehouse leads to increased storage costs.

To address these challenges, the Lakehouse architecture was introduced, primarily by Databricks.

A Lakehouse combines the benefits of both a data lake and a data warehouse. It uses low-cost storage (e.g., object storage) while providing traditional data warehouse features like ACID transactions, versioning, caching, and query optimization.

What differentiates Lakehouse from previous attempts to bring processing to data lakes is the introduction of more efficient metadata layers. For example:

  • Delta Lake by Databricks
  • Iceberg by Netflix for managing analytics data more effectively on S3
  • Hudi by Uber for enabling data upserts and incremental processing in the data lake.

In Lakehouse architecture, all data operations go through open table formats to provide essential data warehouse features such as table snapshotting, time travel, schema evolution, and partitioning. These formats also record statistics to help query engines optimize performance by pruning unnecessary data, such as by checking the min/max values of columns.

Interview Questions for Data Lake , Datawarehouse, Lakehouse

Here are some interview questions for Data Lake, Data Warehouse, and Lakehouse:

Data Lake:

  1. What is a Data Lake and how is it different from a Data Warehouse?
  2. Explain the types of data that can be stored in a Data Lake.
  3. What are the advantages of using a Data Lake for big data storage?
  4. How do you ensure data quality and consistency in a Data Lake?
  5. What challenges do Data Lakes face when working with unstructured data?
  6. How do you handle schema enforcement in a Data Lake?
  7. What are some common tools or frameworks used to work with Data Lakes (e.g., Hadoop, Spark, etc.)?
  8. Can you explain the concept of a “Data Swamp” and how to avoid it in a Data Lake architecture?
  9. How does data ingestion in a Data Lake differ from traditional ETL processes?
  10. What are the security concerns when working with Data Lakes, and how can they be mitigated?
 

Data Warehouse:

  1. What is a Data Warehouse and how does it differ from a Data Lake?
  2. Can you explain the ETL process and its role in a Data Warehouse?
  3. What are the key benefits of using a Data Warehouse for analytics?
  4. What is meant by “Schema-on-Write” in a Data Warehouse, and how does it differ from “Schema-on-Read”?
  5. How do you optimize the performance of a Data Warehouse?
  6. What is OLAP, and how does it relate to a Data Warehouse?
  7. What are some common data modeling techniques used in Data Warehouses (e.g., star schema, snowflake schema)?
  8. How do you ensure data integrity and consistency in a Data Warehouse?
  9. What is the role of indexing in a Data Warehouse, and how does it affect query performance?
  10. How do Data Warehouses handle historical data, and how do they support time-travel queries?
 

Lakehouse:

  1. What is a Lakehouse, and how does it combine features of both Data Lakes and Data Warehouses?
  2. What are the main benefits of using a Lakehouse over separate Data Lakes and Data Warehouses?
  3. Can you explain how Delta Lake (or Iceberg, Hudi) works in the context of a Lakehouse?
  4. What is ACID compliance, and why is it important in the context of a Lakehouse?
  5. How does a Lakehouse handle schema evolution and partitioning?
  6. What are some use cases where a Lakehouse architecture would be more beneficial than a traditional Data Warehouse or Data Lake?
  7. What are the performance considerations when using a Lakehouse, especially when dealing with large datasets?
  8. How does the Lakehouse architecture handle data quality, and what tools are used to ensure this?
  9. Can you explain the concept of “time travel” in a Lakehouse and how it benefits data analysis?
  10. What are the cost implications of using a Lakehouse compared to using separate Data Lakes and Data Warehouses?

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