What is Databricks? | Introduction to Databricks | Edureka

Summary

This 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

00:00

🧩 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.

05:01

🛠️ 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.

Keywords

💡Databricks

Databricks is a unified platform for data engineering and machine learning, offering tools for data processing and analytics. It was founded by the creators of Apache Spark and has become a significant player in big data and machine learning. In the video, Databricks is described as a 'puzzle-solving body' that helps assemble data pieces into a coherent picture, emphasizing its role in making sense of vast amounts of data.

💡Apache Spark

Apache Spark is an open-source distributed computing system that facilitates fast and general-purpose cluster-computing. It is integral to Databricks, as mentioned in the script, and is used for complex data processing tasks. The video highlights that Databricks integrates with Apache Spark, allowing for efficient data engineering and analytics.

💡MLflow

MLflow is an open-source platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. It is one of the significant contributions from the founders of Databricks. The video script mentions MLflow as a tool that Databricks provides for data scientists to track experiments and streamline the machine learning development process.

💡Delta Lake

Delta Lake is an open-source storage layer that brings reliability to data lakes by adding features like ACID transactions to Apache Spark. It is described in the script as a component that ensures reliability, performance, and security in Databricks' architecture, working over existing data and being compatible with APIs like Spark and Hive.

💡Data Scientists

Data scientists are professionals who use Databricks for developing and training machine learning models, running data analysis, and visualizing results. The video emphasizes the collaborative workspace and integration with popular machine learning libraries that Databricks provides to support their work.

💡Data Engineers

Data engineers use Databricks for tasks such as transforming and cleaning data, creating data pipelines, and optimizing data for analysis. The video script mentions that Databricks supports a path for big data processing and offers integration with various data resources, which is crucial for data engineers.

💡Data Analysts

Data analysts explore data, create visualizations, and dashboards using Databricks. The script highlights that Databricks supports SQL, interactive notebooks, and a wide array of visualization options, which are essential tools for data analysts to perform their tasks.

💡Data Lakehouse Architecture

The Data Lakehouse architecture combines the best features of data lakes and data warehouses, as described in the video. It consists of three main layers: the bronze layer for raw data, the silver layer for metadata and governance, and the golden layer for BI reports, data science, and machine learning. This architecture is underpinned by Delta Lake in Databricks.

💡ACID Transactions

ACID transactions are a set of properties that guarantee the reliability of database transactions. They ensure that transactions are Atomic, Consistent, Isolated, and Durable. In the context of the video, Delta Lake provides ACID transactions for data processing, which is crucial for maintaining data integrity and reliability in Databricks.

💡Cloud Services

Cloud services like AWS and Azure are mentioned in the script as platforms where Databricks can be stored and utilized. These services provide the infrastructure for Databricks to operate, allowing for scalable and flexible data processing and analytics in the cloud.

💡Notebooks

Notebooks in Databricks are interactive programming environments that support multiple languages like R, Scala, SQL, and Python. As described in the video, notebooks are used for collaborative work, where data scientists and engineers can write and share code, making them an essential part of the Databricks platform.

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