What is Lakehouse Architecture? Databricks Lakehouse architecture. #databricks #lakehouse #pyspark

Summary

This video tutorial explains the evolution of data warehousing architectures, transitioning from traditional data warehouses to the modern Lakehouse model. It highlights how the Lakehouse architecture, introduced by Databricks with Delta Lake, combines the benefits of both Data Lakes and Data Warehouses. It supports structured, semi-structured, and unstructured data, enabling advanced analytics, machine learning, and traditional reporting. The video also touches on competitors like Iceberg and Hudi and encourages viewers to explore more on Databricks' website.

Takeaways

• 🏠 **Lake House Architecture**: A new approach combining the best of Data Lakes and Data Warehouses.
• 📈 **Evolution of Data Management**: From traditional data warehousing to modern architectures like Data Lakes and Lake House.
• 🔄 **ETL Processes**: Essential for loading data into data warehouses and transforming data in Data Lakes.
• 💾 **Data Storage**: Data Lakes can store structured, semi-structured, and unstructured data, offering unlimited storage.
• 🚀 **Advantages of Data Lakes**: Include flexibility and the ability to handle all types of data.
• 🛑 **Challenges with Data Lakes**: Lack of SQL support, performance tuning, and metadata management.
• 🌊 **Introduction of Lake House**: Databricks introduced the Lake House concept with Delta Lake in 2019.
• 🔄 **Delta Lake**: Enables database operations on Data Lakes, combining the features of both Data Lakes and Data Warehouses.
• 🔧 **Metadata Management**: A key feature of Lake House architecture, improving data lineage and analytics.
• 🌐 **Cloud Compatibility**: Lake House architecture is compatible with various cloud platforms like Amazon, Google, and Azure.
• 📚 **Resources**: Many projects, documents, and success stories are available on the Databricks website.

Q & A

What is Lake House architecture?

Lake House architecture is a modern approach that combines the capabilities of a Data Lake with the features of a Data Warehouse. It allows for the storage of structured, semi-structured, and unstructured data, and provides database operations and features on top of the data lake using technologies like Delta Lake.

What was the common architecture for data warehousing before the introduction of Lake House?

Before Lake House, the common architecture involved structured data sources like ERPs being loaded into a Data Warehouse using ETL tools. The data was then used for reporting and business intelligence purposes by teams like reporting and BI teams.

How did the data warehousing landscape change with the introduction of Data Lakes?

With the introduction of Data Lakes, the landscape shifted to include the storage of any kind of data, including structured, semi-structured, and unstructured. Data Lakes utilized distributed file systems like HDFS and Hadoop, and later cloud-based solutions, providing unlimited storage and the ability to read data directly.

What are the advantages of Data Lakes over traditional Data Warehouses?

Data Lakes offer advantages such as unlimited storage, the ability to store any type of data, and direct access to data. However, they lack certain features like SQL support, performance tuning, and metadata management that are present in traditional Data Warehouses.

What is the primary difference between Data Lake and Lake House architectures?

The primary difference is that Lake House architecture adds database features like SQL support, performance tuning, and metadata management to the capabilities of a Data Lake, making it suitable for both storage and analytics purposes without the need for a separate Data Warehouse.

Who introduced the Lake House architecture?

Lake House architecture was introduced by Databricks in combination with Delta Lake in 2019. This combination allows for the use of Databricks' platform with the Delta Lake technology to enable database operations on top of a Data Lake.

What is Delta Lake and how does it fit into the Lake House architecture?

Delta Lake is an open-source storage layer that enables ACID transactions on top of cloud storage. It fits into the Lake House architecture by providing data reliability and management features like ACID transactions, scalable metadata handling, and unifying data science and big data workloads.

What are the competitors to Delta Lake in the Lake House architecture space?

The competitors to Delta Lake include Iceberg and Apache Hudi. These are also open-source storage layers that provide similar capabilities to Delta Lake, allowing for the management of large-scale data lakes with features like ACID transactions and schema evolution.

How does Lake House architecture support advanced analytics, data science, and machine learning?

Lake House architecture supports advanced analytics, data science, and machine learning by providing a unified platform where raw data can be stored in any format and then processed and transformed into a structured format suitable for these purposes using tools like Delta Lake.

What are the benefits of using Lake House architecture over separate Data Lake and Data Warehouse systems?

Using Lake House architecture provides benefits such as reduced complexity, lower costs, and improved performance due to the unified platform that handles both storage and analytics without the need to move data between separate systems.

Where can one find more information and success stories about Lake House architecture?

More information and success stories about Lake House architecture can be found on the Databricks website, where they provide resources, videos, and documents detailing the architecture and its implementation in various industries.

Outlines

00:00

🏞️ Introduction to Lake House Architecture

The script discusses the evolution of data warehousing architecture, transitioning from traditional data warehousing to the concept of Lake House. Traditional data warehousing relied on structured data sources like ERPs and OLTP databases, using ETL tools to load data into data warehouse databases. This architecture has been prevalent for the past 30 years. However, with the advent of data lakes in the late 2000s, the landscape changed. Data lakes, enabled by technologies like Hadoop and HDFS, allowed for the storage of structured, semi-structured, and unstructured data. This was followed by the emergence of cloud data lakes, which offered unlimited storage and the ability to read data directly. Despite their advantages, data lakes faced challenges with SQL performance tuning and metadata management. The Lake House architecture, introduced by Databricks with Delta Lake in 2019, combines the best of both worlds by overlaying data lake capabilities with data warehouse features, offering a unified platform for structured and unstructured data with enhanced SQL performance and metadata management.

05:01

🌐 Modern Data Warehousing and Lake House

This section delves into the modern data warehousing architecture, contrasting it with the traditional model. Modern data warehousing involves the use of cloud-based solutions like Snowflake, Azure SQL, BigQuery, and Redshift, which can handle both structured and unstructured data. The Lake House architecture is highlighted as the latest advancement, where data is stored in a data lake and managed by a data warehouse system. The script emphasizes that with Lake House, there is no need for separate data lakes and warehouses; instead, everything is managed within the data lake environment. Delta Lake enables database features on top of the data lake, allowing for batch processing, streaming, BI, data science, and machine learning. It also addresses the competitors of Delta Lake, such as Iceberg and Apache Hudi, and mentions that Databricks offers Delta Lake as the default option for Lake House architecture. The Lake House architecture is positioned as a comprehensive solution that provides both data lake and data warehouse functionalities, along with metadata catalog support for data lineage and multiple catalogs.

10:02

📢 Conclusion and Resources for Lake House Architecture

The final paragraph serves as a conclusion to the video script, summarizing the key points about Lake House architecture and inviting viewers to explore more resources. It mentions that there are numerous projects, documents, and videos available on the Databricks website and through a simple Google search. The speaker encourages viewers to visit the Databricks website or search for 'Databricks success stories' to learn from customer implementations. The paragraph also touches on the availability of Databricks across all major cloud platforms, combining Spark and Spark SQL to manage Lake House projects effectively.

Keywords

💡Lake House Architecture

Lake House Architecture is a modern approach to managing data that combines the best aspects of data lakes and data warehouses. It allows for the storage of structured, semi-structured, and unstructured data in a single location, while also providing the ability to perform SQL operations and other database functionalities. In the video, the speaker discusses how this architecture emerged as an evolution from traditional data warehousing and data lakes, emphasizing its role in simplifying data management and enabling advanced analytics.

💡Data Lake

A Data Lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. It is designed to handle structured, semi-structured, and unstructured data. The video mentions how data lakes came into the picture in the late 2000s, primarily as a means to store data using distributed file systems like Hadoop HDFS. The data lake concept is central to the Lake House Architecture as it forms the foundation for storing diverse types of data.

💡Data Warehouse

A Data Warehouse is a system used for reporting and data analysis. It stores subject-oriented, integrated, non-volatile, and time-variant data. The script explains how traditional data warehousing projects have been around for the last 30 years, primarily dealing with structured data from sources like ERP systems. The data warehouse is a key component in traditional architectures and is contrasted with the more flexible data lake concept.

💡ETL

ETL stands for Extract, Transform, Load, a process in database usage which extracts data from outside sources, transforms it to fit into the organization's database, and loads it into the organization's database. The video script describes how ETL tools are used to load data into data warehouses from structured databases and also to prepare data from data lakes for use in data warehouses.

💡Structured Data

Structured data refers to information that is organized into a formatted repository, typically a database, and is often typecast (e.g., numbers, text, dates). The script discusses how traditional data warehousing primarily deals with structured data from sources like ERP systems, and how Lake House Architecture can handle structured data along with semi-structured and unstructured data.

💡Semi-Structured Data

Semi-structured data is data that has some structure, but does not adhere to the rigid structure of a typical database. It may include formats such as XML, JSON, and CSV. The video explains that Lake House Architecture allows for the storage and processing of semi-structured data, which is a departure from traditional data warehousing practices.

💡Unstructured Data

Unstructured data is data that does not have a predefined data model or is not organized in a pre-defined manner. It can be text files, emails, documents, etc. The video mentions that data lakes are designed to store unstructured data, and the Lake House Architecture extends this capability, allowing for the analysis of unstructured data.

💡Delta Lake

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. It enables data to be stored in a data lake, but with the reliability and performance of a data warehouse system. The script discusses how Delta Lake is used on top of data lakes in the Lake House Architecture to provide database operations and features, thus combining the benefits of data lakes and data warehouses.

💡Databricks

Databricks is a company founded by the creators of Apache Spark, which provides a unified analytics platform for big data processing. The video mentions Databricks as a key player in the development of the Lake House Architecture, particularly in combination with Delta Lake. Databricks provides tools and services that enable the implementation of Lake House Architecture.

💡Metadata Management

Metadata Management is the process of defining and managing metadata, data about data, within an organization. The video script points out that metadata management is a key aspect of data warehousing that was lacking in traditional data lakes. Lake House Architecture, with Delta Lake, addresses this by providing robust metadata management capabilities.

💡Data Catalog

A Data Catalog is a registry of all the data an organization collects, along with details about where it is stored, its quality, who owns it, and other relevant information. The video discusses how the Lake House Architecture incorporates data catalog features, which are essential for managing and discovering data across the organization.

Highlights

Introduction to Lake House architecture and its significance in the industry over the last two years.

Explanation of the traditional data warehousing architecture and its evolution from the 1980s to the present.

Description of how data is loaded from structured databases into data warehouses using ETL tools in traditional systems.

The shift to Data Lake architecture in the 2010s, with the introduction of Big Data and distributed file systems like Hadoop.

Capability of Data Lakes to store structured, semi-structured, and unstructured data both on-premises and in the cloud.

The use of ETL tools in Data Lakes, including Scala, Java, and Spark, for processing data into a structured format for data warehouses.

Advantages of Data Lakes, such as unlimited storage and the ability to store any type of data.

Disadvantages of Data Lakes, including the lack of SQL support, performance tuning, and metadata management.

Introduction of Lake House architecture by Databricks in 2019, combining the features of Data Lakes and Data Warehouses.

How Delta Lake enables database operations on top of Data Lakes, providing a combination of Data Lake and Data Warehouse features.

The elimination of the need for separate data warehouses in Lake House architecture.

Description of the modern Data Lake architecture and its components, including cloud-based data warehouses.

The role of Delta Lake in enabling database features like metadata management, massive transactions, and DML operations.

The metadata catalog provided by Delta Lake and its importance for data lineage and analytics.

Competitors to Delta Lake, such as Iceberg and Apache Hudi, and their roles in the Lake House ecosystem.

The availability of Lake House architecture in Databricks across all major cloud platforms.

Resources for further learning about Lake House architecture, including videos, documents, and success stories on Databricks' website.

The impact of Lake House architecture on industry projects and its practical applications.

Transcripts

to take like video tutorials so in this

video I'm going to give you brief

information about lake house

architecture like last two years if you

observed in Industry so most of the

people are talking about lake house and

what is exactly lake house architecture

and what was before lake house like data

Lake and data warehouse so that we will

understand in today's session

so when you compare from last 40 years

from 1980s to till there are different

kinds of architecture and modern and

traditional data warehousing projects

modern and traditional data virusing

projects

when it comes to late uh 80s like uh

this data virus architecture introduced

in 1980s from 1990 onwards you can see

most of the dbms and rdbms following the

same kind of architecture

so traditional data warehousing projects

the sources will be most of the sources

will be structured databases like erps

erps databases

olpp databases

databases using ETL tool we load data

into date of arrows that data variable

as olav Warehouse database

then any reporting team RBI team they

will extract the data and they will

provide reports to the business people

this is a common architecture from last

30 years so most of the projects uh are

using and the primary data warehouse

primary sources and Target as structured

data

source is also structured data Target

also structure data

then 2010 and 2011 when you compare data

Lake came into the picture mainly on

on-premises Big Data combination or

distributed file system hdfs Hadoop

radius then later Spar came into the

picture

so where a data Lake can store any kind

of data structure same structure

unstructured when it comes to

on-premises our cloud data lake is a

common concept on-premises means hdfs

where you can store structured

semi-structured unstructured data

and then once data is available on data

Lake again we will use some ETL tool of

ETL tool is nothing but when it comes to

on-premises more Scala Java based

projects even Pi spark also is there but

less projects on on premises but when it

comes to Cloud more Iceberg projects

then we will read the data and we will

convert into Data Warehouse again like

we will convert into structured format

and we load data into separate data

values

then traditional reporting team they

will take a well-connected

they'll use the reporting purpose

when it comes to Advanced analytics data

science and machine learning they will

read data from data Lake

and data Lake where you can store any

kind of data and data like is having a

major advantages like unlimited storage

any kind of data any type of data okay

and directly you can read from data link

but the major disadvantage is here when

it comes to data Lake data lake is

having a of major features plus

disadvantages like data various

disadvant data various advantages here

disadvantages mean

databases the major advantage is SQL

Performance Tuning

Optimizer will be the SQL Optimizer will

be there in your database DML operations

it will support

absurds insert upsets insert update

delete merge everything you can use in

data various databases but those are

missing here metadata management is a

key important thing when it comes to

analytics part so metadata management

also missing on data Lake

so in 2020 actually lake house actually

Legos data bricks with the Delta

combination they introduced in 2019

2019 lake house architecture the

combination of data bricks plus Delta

lake so your data will be there on data

Lake your data will be there on data

Lake

on top of data Lake we will be using a

Delta lake with the data bricks

and the Delta will enable you database

operations database features on top of

data Lake you will get all features so

your data various features your data

Lake features you will be getting

both is a combination of data Lake plus

Delta lake so that is called lake house

architecture data of arrows plus data

Lake features we will be getting here we

don't need a separate way of you don't

need a separate Warehouse so you don't

need a separate various everything will

be there on data Lake with the Delta I

will convert into unstructured

semi-structured structured format into

analytics purpose

let's understand each level let's

understand each level this is a

traditional data where hosting projects

architecture traditional databases

sources will be erps

most of the erps and that data will that

is operational data Erp is data means

operational data that data will store

into warehouses data warehouses maybe

any data values Oracle teradata db2 SQL

Server MySQL there are plenty of data

viruses are available then reporting

team will take care this is traditional

data warehousing project architecture

from past 30 years you can you can find

this kind of architecture on on premises

then modern

so modern data Lake architecture data

Lake architecture means where

you will be having data and data Lake

and when it comes to cloud cloud is

having a snowflake Azure SQL bigquery

redshift and high even high was is there

is there an on-premises and Cloud as

well

where separate Warehouse

these are our separate values where they

can use for reporting purpose

nothing but this is our data warehouses

in Cloud even on-premises so separate

data like separate data various

but you you are going to manage data

Lake separate we are going to manage

various operate depends on cloud if it

is Amazon redshift Google bigquery Azure

SQL data warehouse third party is

snowflake

so

the latest Lakehouse architecture you

don't need a separate Wireless

everything you can go with the modern

date of arrows everything you can go

with modern data warehouse with

this architecture

structured seven structured unstructured

data will be there then on top of the

Delta Lake will be there then Delta Lake

will enable you it will convert all this

structured semester transaction into

structured format will enable your

database features then you can use for

batch purpose streaming purpose bi data

science machine learning

and metadata catalog will be there

and which you can go with any any kind

of

Analytics

which you can go with the data science

machine learning or SQL analytics are

ready streaming our batch process and

that Delta Lake will enable you on top

of data Lake on data Lake the data may

be structured unstructured

semi-structured and your Delta will

convert into structured format that data

you can use for so the data always will

be there and data Lake data Lake place

Delta Lake combination you will get all

features

and you can find the competitors for

Delta Lake Iceberg and Apache hoodie

these two are competitors for Delta Lake

but the default when it comes to

databricks you will get a Delta Lake now

I would say databricks if you want to

use the Lakers architecture you can go

with iceberg

so your data

will be there on data Lake only on top

of data Lake Delta Lake will be enabling

metadata caching index layout all DML

operations

okay metadata management

massive transactions everything will be

enabled select database features

I mean metadata catalog will be there

that databricks old old database uh Spar

catalog and the new database Unity

catalog so that catalog will enable you

data lineage and multiple catalog

multiple of catalogs like if if you go

with the unity catalog so you can go

with the multiple like data mites kind

of Mars kind of a design you can work

with this

so this is about lake house architecture

lake house architecture you will be

getting a data Lake features

you will be getting various features

that various features means a Delta

Delta Lake will enable you on top of

data Lake Delta Lake will enable you on

top of data Lake

all the features but we are looking for

data warehouse features

so date of arrows plus data lake is

nothing but a lake house nothing but

lake house so this is about

brief information about lake house

architecture and you can find a lot of

projects in databricks you can find you

can just Google it go to the data bricks

and database Solutions you can find a

lot of videos and documents on layouts

architecture even a lot of success

stories also available just you can go

to the databricks website and uh query

or Google it just databricks

success stories

so a lot of customer stories they

already implemented a lot of projects

you can find a lot of customer service

yeah related to lake house

we can go with industry wise

okay a lot of projects and documents

videos are available in this portal

so this is about lake house

and feature will be the lake house and

when it comes to data bricks is

available in all the clouds so

databricks combination spark spark SQL

Pi spark combination which you can go

and manage Legos project

this is about basic information to Legos

architecture if you like this video

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