What is Lakehouse Architecture? Databricks Lakehouse architecture. #databricks #lakehouse #pyspark
Summary
TLDRThis 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
🏞️ 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.
🌐 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.
📢 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.
Mindmap
Keywords
💡Lake House Architecture
💡Data Lake
💡Data Warehouse
💡ETL
💡Structured Data
💡Semi-Structured Data
💡Unstructured Data
💡Delta Lake
💡Databricks
💡Metadata Management
💡Data Catalog
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
please subscribe my channel see you in
another video thank you have a good day
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