What is Databricks? | Introduction to Databricks | Edureka
Summary
TLDRThis video introduces Databricks, a platform founded by the creators of Apache Spark, designed for big data and machine learning. It highlights the platform's capabilities for data scientists, engineers, and analysts, showcasing its collaborative workspace, integration with MLflow, and Delta Lake for data reliability. The video covers the architecture of Databricks, including its data lake house concept with bronze, silver, and gold layers, and explains how it integrates with cloud services. It also demonstrates creating clusters and notebooks, and touches on data engineering and machine learning functionalities within the platform.
Takeaways
- 🧩 **Databricks as a Puzzle Solver**: Databricks is likened to a puzzle solver that helps assemble data pieces into a coherent picture, emphasizing its role in managing data complexity.
- 🌟 **Founders of Databricks**: Databricks was founded by the creators of Apache Spark, highlighting the company's strong technical background in distributed computing systems.
- 📈 **Significant Contributions**: The founders have contributed significantly to the field with innovations like MLflow for machine learning lifecycle management and Delta Lake for reliable data lakes.
- 👨🔬 **Data Scientists' Toolkit**: Databricks serves data scientists by providing a collaborative workspace, integration with ML libraries, and notebook-based environments for model development and data analysis.
- 🔧 **Data Engineers' Ally**: It supports data engineers in transforming and cleaning data, creating pipelines, and optimizing data for analysis through its integration with big data processing tools.
- 📊 **Data Analysts' Helper**: Databricks aids data analysts in data exploration, visualization, and dashboard creation, offering SQL, interactive notebooks, and a variety of visualization options.
- 🏗️ **Data Lakehouse Architecture**: The platform's architecture is a unified Data Lakehouse, combining features of data lakes and warehouses, structured into bronze, silver, and gold layers for data processing and analysis.
- 💧 **Delta Lake Foundation**: Delta Lake underpins the architecture, ensuring data reliability, performance, and security with ACID transactions and scalable metadata handling.
- ☁️ **Cloud Service Integration**: Databricks is designed to be stored and operated on popular cloud services like AWS and Azure, facilitating cloud-based data processing and analytics.
- 🛠️ **Implementation and Usage**: The script outlines the implementation process, including creating clusters for distributed computation, using notebooks for collaborative coding, and the platform's support for various data sources and ML frameworks.
Q & A
What is Databricks and what does it do?
-Databricks is a platform that helps to assemble data pieces together, much like solving a puzzle, to create a coherent picture. It is used for data engineering, machine learning, and analytics, allowing users to clean, transform, analyze, and visualize data efficiently.
Who founded Databricks and what is its connection to Apache Spark?
-Databricks was founded by the creators of Apache Spark, an open-source distributed computing system. The company was established in 2013 and has become a significant player in big data and machine learning.
What are the other contributions from the founders of Databricks besides Apache Spark?
-The founders of Databricks have also contributed MLflow, an open-source platform for managing the machine learning lifecycle, and Delta Lake, an open-source storage layer that brings reliability to data lakes.
Who are the primary users of Databricks and what do they use it for?
-The primary users of Databricks include data scientists, data engineers, and data analysts. Data scientists use it for developing and training machine learning models, data engineers for transforming and cleaning data, and data analysts for exploring data and creating visualizations.
Can you explain the architecture of Databricks, known as the Data Lakehouse?
-The Data Lakehouse architecture of Databricks is a unified platform that combines the features of data lakes and data warehouses. It consists of three layers: the bronze layer for raw data, the silver layer for metadata and governance, and the gold layer for BI reports, data science, and machine learning.
What role does Delta Lake play in the Databricks architecture?
-Delta Lake underpins the Databricks architecture by ensuring reliability, performance, and security. It provides ACID transactions and scalable metadata handling, which are crucial for data engineering and analytics.
How does Databricks integrate with cloud services?
-Databricks can be stored and utilized on famous cloud services like AWS and Azure, allowing for seamless integration and scalability for data processing and analytics tasks.
What are the two main divisions of platforms in Databricks and what do they offer?
-Databricks has two main divisions: data engineering and machine learning. The data engineering platform integrates with Apache Spark for complex data processing tasks, while the machine learning platform offers a collaborative environment for building, training, and deploying machine learning models.
How does one create a cluster in Databricks?
-To create a cluster in Databricks, one needs to log in to their account, click on the Clusters tab, and then click on the create cluster button. Users can name the cluster, choose a runtime version, and set the number of users working in the cluster.
What is a Notebook in Databricks and how is it used?
-A Notebook in Databricks is a collaborative environment for writing and running code. It supports multiple programming languages and is used for tasks such as data analysis, visualization, and model development.
How can users manage their resources like clusters and notebooks in Databricks?
-Users can manage their resources in Databricks by creating, deleting, or cloning clusters and notebooks as needed. They can also organize their work using directories and share files through the export option.
Outlines
🧩 Introduction to Databricks and Its Founding
This paragraph introduces the concept of data as a collection of pieces that need to be assembled to form a coherent picture, likening it to a puzzle. It then introduces Databricks as a tool that helps in solving this puzzle. The paragraph welcomes viewers to the YouTube channel and encourages new viewers to subscribe and use the bell icon to stay updated. It also suggests taking up a Databricks training course for those interested in the topic, with a link provided in the description. Databricks is described as a company founded by the creators of Apache Spark in 2013, and it has since become a significant player in big data and machine learning. The paragraph also mentions other contributions by the founders, including MLflow and Delta Lake, which are open-source platforms for managing the machine learning lifecycle and providing reliability to data lakes, respectively. The paragraph concludes by outlining who can use Databricks and why, including data scientists, data engineers, and data analysts, and briefly describes the architecture of Databricks, known as the Data Lakehouse architecture, which combines the best features of data lakes and data warehouses.
🛠️ Databricks Implementation and User Experience
This paragraph delves into the practical aspects of using Databricks, focusing on the creation of clusters, notebooks, and tables. It explains that clusters in Databricks are groups of computers that work together to perform tasks, and it provides a step-by-step guide on how to create a cluster within the Databricks platform. The paragraph also discusses the collaborative aspect of Databricks through notebooks, where users can write and execute code. It mentions the ability to choose different programming languages like R, Scala, and SQL according to user needs. Additionally, it covers the process of creating tables and selecting data resources from various sources such as DBFS, S3, or other cloud storage options. The paragraph concludes with a call to action for viewers to engage with the content by commenting, liking, and subscribing for more educational videos on the topic.
Mindmap
Keywords
💡Databricks
💡Apache Spark
💡MLflow
💡Delta Lake
💡Data Scientists
💡Data Engineers
💡Data Analysts
💡Data Lakehouse Architecture
💡ACID Transactions
💡Cloud Services
💡Notebooks
Highlights
Databricks is a platform that helps assemble data pieces like a puzzle.
Founded by the creators of Apache Spark in 2013.
Significant player in big data and machine learning spaces.
Apache Spark is an open-source distributed computing system.
MLflow is an open-source platform for managing the machine learning lifecycle.
Delta Lake is an open-source storage layer for reliable data lakes.
Databricks offers a collaborative workspace for data scientists.
Data Engineers use it for data transformation and pipeline creation.
Data Analysts use it for data exploration and visualization.
Data Lakehouse architecture combines features of data lakes and warehouses.
Bronze layer is the raw data layer for unprocessed data.
Silver layer processes raw data into a consumable format.
Gold layer is for aggregated and optimized data for reporting and analytics.
Delta Lake ensures reliability, performance, and security in the architecture.
Databricks integrates with Apache Spark for complex data processing tasks.
MLflow is used for tracking experiments and model versioning in machine learning.
Databricks supports a wide range of machine learning frameworks and libraries.
Clusters in Databricks are groups of computers for distributed computations.
Notebooks in Databricks provide a collaborative environment for code development.
Databricks supports multiple programming languages like R, Scala, and SQL.
Users can create, clone, export, or delete notebooks and clusters in Databricks.
Transcripts
foreign
[Music]
of pieces and you need to put them
together to create a beautiful picture
that's what data can be like lots of
little pieces that need to be assembled
to make sense databricks is like your
puzzle solving body that helps you to
put all those pieces together
hello and welcome back to our YouTube
channel if you are joining us for the
first time don't forget to hit the
Subscribe button and the bell icon so
you won't miss out any of our exciting
content
and also I will suggest you to take up
the purchase Park training course if you
are interested in this topic the link is
present in the description below now
let's start with the topic of our video
what is databricks but wait before that
first we need to know who founded
databricks databricks was founded by the
creators of Apache spark which is an
open source distributed computing system
they founded the company in 2013 and it
has since become a significant player in
the big data and machine learning spaces
some of the other important
contributions from the founders are MN
flow and Delta Lake ml flow is an open
source platform that manages the machine
learning life cycle including
experimentation reproducibility and
deployment it provides various
components to streamline the end-to-end
development process while on the other
hand Delta lake is an open source
storage layer that brings reliability to
data Lakes it works over your existing
data and is fully compatible with apis
like spark Hive and providing AC
transactions using streaming and batch
data processing it provides the platform
for data engineering machine learning
and Analytics now we have to see who all
can use data breaks and why first of all
we will start with data scientists they
use it for developing and training
machine learning models running data
analysis and visualizing results and it
provides a collaborative workspace
integration with popular machine
learning libraries and notebook-based
programming environments second we have
data Engineers they use it for
transforming and cleaning data creating
data pipelines and optimizing data for
analysis it supports a purchase path for
big data processing and offers
integration with various data resources
and syncs third and the last one we have
data analysis they use it for exploring
data creating visualizations and
dashboards it also used for running ad
hoc queries it supports SQL interactive
notebooks and wide array of
visualization options
as we have already seen who all can use
data breaks let's start with the
architecture part the data lake house
architecture is a unified platform that
combines the best features of data links
and data warehouses it's built on three
main layers as you can see from the
picture the first layer is the bronze
layer the structured semi-structured and
unstructured data the second one is
silver layer as you can see from the
picture again it is showing metadata and
governance layer that is the silver
layer and the third one is a golden
layer as you can see they are mentioned
bi reports data science machine learning
that is the golden layer so first we
will start with the bronze layer this is
the raw data layer where data from
various sources in ingested in its
native format it acts as a staging area
for unprocessed data second we have
silver layer here the raw data is clean
processed and transformed into a more
consumable format it serves as a bridge
between the raw and refined data
providing a clean version of our
analysis the last one is the gold layer
the final layer where data is further
aggregated and optimized for reporting
and analytics it offers a ready-to-use
high quality data set for business users
underpinning the architecture the Delta
Lake which ensures reliability
performance and security it provides AC
transactions and scalable metadata
handling finally it will be stored in
the few of the famous cloud services
like AWS and Azure now we will move on
to the implementation part
for that first we have to discuss that
databricks has two division of platforms
first one is data engineering and the
second one is machine learning first
we'll start with the data engineering
part it integrates with Apache spark a
leading open source Computing system
allowing data engineers and scientists
to perform complex data processing tasks
the platform's data engineering
capabilities enable users to clean
transform and analyze vast amount of
data efficiently users can work with
various data sources including
relational databases nosql stores and
cloud storage seamlessly integrating
them into their analytics workflows and
on the machine learning side data breaks
offers a collaborative environment where
data scientists can build train and
deploy machine learning models by
providing access to ml flow of popular
open source machine learning lifecycle
tool it enables tracking of experiments
model versioning and streamline
deployment the platform also supports a
wide range of machine learning
Frameworks and libraries such as
tensorflow pytorque that offers
flexibility in model development in both
the platforms we can create tables
notebooks clusters experiments and
models
so we will start with the first one
which is creating the cluster part
so what are clusters clusters in
databricks are groups of computers that
work together to perform tasks by
creating clusters we can distribute our
computations across multiple machines
creating a cluster in databricks is
quite simple let me show you how first
you will need to login to your
databricks account click on the Clusters
tab on the left hand side now as you can
see from the screen you can create a
cluster by clicking on the create
cluster button the first we will name as
I will be showing student database in
this video so I will be naming it as
student database and it is also asking
for the runtime version so basically the
runtime version depends on the
compatibility of your system and larger
the version larger limited data sets as
you can see so the cluster has already
been created and you can also set the
number of users which will be working in
the Clusters now let's start the
collaborative part of the databricks
which is The Notebook let's create one
as you can see in the screen the
environment is already set up we just
need to put our code here so for that
let me first paste the code here now
let's run the code
as usual it's taking time as we are
making a database for my input code and
they will arrange the student database
as I have mentioned in the code
there are many options like new notebook
or clone or export to DBC archive which
you can see from the screen
we can also share files by clicking on
the export option
as programming languages are concerned
we have also options in programming
languages like R Scala SQL as per our
need we can use the programming
languages to run our code
now we will create tables but as you can
see we have to choose data resources
from the dbfs path or S3 or other
resources in the options present in the
screen so what are the other resources
they are the resources like Kinesis
Cassandra Etc and S3 as well as dbfs
which can be used to create the
directory
sometimes it happens that we have create
a lot of clusters or notebooks so the
directory has been filled so for that
purpose we can also delete few of them
as you can see from the screen we just
have to click on the clustered file and
we can delete or clone it easily so
these are the few things you can start
by using databricks and there are few
more remarkable ones which we will be
discussing in the next video if you are
interested in knowing those remarkable
features don't forget to comment your
views until then Happy learning I hope
you have enjoyed listening to this video
please be kind enough to like it and you
can comment any of your doubts and
queries and we will reply them at the
earliest do look out for more videos in
our playlist And subscribe to edureka
channel to learn more happy learning
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