Skip to main content

AWS : Running spark code using Databricks

In this blog, we are going to understand how to prepare Databricks workbench to read, write data from relational databases in cloud(AWS) environment. Databricks is a company that provides could based platform for data analytics and AI. We have to create an account here which is free of cost.


What is the use case ? 

We are basically trying to read data from a MySQL table which is residing inside a MySQL DB created in AWS RDS service. Once we read the table/data, we need to apply a simple transformation and load same table in another SQL DB i.e., MicrosoftSQL which is also residing inside AWS RDS service. So, first we need to create 2 databases, one MySQL and another MSSQL by logging in into AWS account. 

Once these 2 databases are ready, we need to install SQL workbench to interact with these relational databases from our local system(Please refer : How to setup SQL workbench blog)


How to create Databricks account ?

  • Login to https://community.cloud.databricks.com/ using email ID
  • You will receive an OTP to email, enter it in the prompt and login
  • Reference screenshot 


How to create a cluster with Spark environment (refer above screenshot) ?
  • Click on compute option
  • Click on "Create compute" button
  • Now select some random version of spark and click create compute button
  • That's all, cluster with spark environment have created

How to open a new note to write Spark code ?
  • Click on "New" button on the top and then create a new notebook


Scenario 1 :  Read table from MySQL
  • Open SQL workbench, connect to MySQL database inside AWS RDS(refer section "How to open a new workbench and connect to a Database ?" inside my blob "How to setup SQL workbench" 
Spark code :

# Below line of code is the connection URL of MySQL database inside AWS RDS service
data="jdbc:mysql://sparkpoc1.ctucw2ay6r46.ap-south-1.rds.amazonaws.com:3306/arundb"

# This is how we connect to a specific table using spark, check query section in spark website : https://spark.apache.org/docs/3.5.1/sql-data-sources-jdbc.html
df=spark.read.format("jdbc").option("url", data).option("user","admin").option("password","Mypassword.01").option("dbtable","emp").load()

# To display the data that we read using above line of code
df.show()


Scenario 2 :  Read table from MySQL and load same table in another database, MSSQL

Spark code :

from pyspark.sql.functions import *
# MySQL URL
data="jdbc:mysql://sparkpoc1.ctucw2ay6r46.ap-south-1.rds.amazonaws.com:3306/arundb"
# reading emp table from MySQL
df=spark.read.format("jdbc").option("url", data).option("user","admin").option("password","Mypassword.01").option("dbtable","emp").load()
# displaying information from data frame
#df.show()
# Transformation
df=df.withColumn("today", current_date())
# MSSQL URL
mssqlurl="jdbc:sqlserver://sparkpoc2.ctucw2ay6r46.ap-south-1.rds.amazonaws.com:1433;trustServerCertificate=true;databaseName=arundb"
# Loading emp table read from MySQL DB to arunemp table in MSSQL table
df.write.format("jdbc").option("url", mssqlurl).option("user","admin").option("password","Mypassword.02").option("dbtable","arun1emp").save()


Scenario 3 :  Reading all tables from a DB in MySQl, transform and load it into destination DB MSSQL

Spark code :

from pyspark.sql.functions import *

#tabs=["emp","dept","orders"]

data="jdbc:mysql://walmart2025.cdyg20qqucui.ap-south-1.rds.amazonaws.com:3306/pavanidb"
qry="(SELECT table_name FROM information_schema.tables WHERE table_schema = 'pavanidb') aaa"
all=spark.read.format("jdbc").option("url",data).option("user","myuser").option("password","Mypassword.1").option("dbtable",qry).load()
type(all)
print(all)
tabs = [t[0] for t in all.collect()]

print(tabs)
for t in tabs:
print("table:",t)
data="jdbc:mysql://walmart2025.cdyg20qqucui.ap-south-1.rds.amazonaws.com:3306/pavanidb"
df=spark.read.format("jdbc").option("url",data).option("user","myuser").option("password","Mypassword.1").option("dbtable",t).load()
df.show(4)
#df.write.format("jdbc").option("url",mshost).option("user","myuser").option("password","Mypassword.1").option("dbtable",t+"_mysql123").save()



Arun Mathe

Gmail ID : arunkumar.mathe@gmail.com


Labels:

Comments

Post a Comment

Popular posts from this blog

(AI #1) Deep Learning and Neural Networks

I was curious to learn Artificial Intelligence and thinking what is the best place to start learning, and then realized that Deep Learning and Neural Networks is the heart of AI. Hence started diving into AI from this point. Starting from today, I will write continuous blogs on AI, especially Gen AI & Agentic AI. Incase if you are interested on above topics then please watch out this space. What is Artificial Intelligence, Machine Learning & Deep Learning ? AI can be described as the effort to automate intellectual tasks normally performed by Humans. Is this really possible ? For example, when we see an image with our eyes, we will identify it within a fraction of milliseconds. Isn't it ? For a computer, is it possible to do the same within same time limit ? That's the power we are talking about. To be honest, things seems to be far advanced than we actually thing about AI.  BTW, starting from this blog, it is not just a technical journal, we talk about internals here. ...
Post a Comment
Read more

(AI #3) Deep Learning Foundations - Activation & Loss Functions, Gradient Descent algorithms & Optimization techniques

It is extremely important to have a deep knowledge while designing a machine learning model, otherwise we will end up creating ML models which are of no use. We have to have a clear understanding on certain techniques to confidently build a ML model, train it using "training data", finalize the model and to deploy it in production. So far, from blog #1, #2, we have seen about the fundamentals of Deep Learning and Neural Network, architecture of a Neural Network, internal layers and components etc.  Providing the links of Blogs #1 , #2 below for quick reference. Deep Learning & Neural Networks : https://arunsdatasphere.blogspot.com/2026/01/deep-learning-and-neural-networks.html Building a real world neural network: A practical usecase explained : https://arunsdatasphere.blogspot.com/2026/01/building-real-world-neural-network.html Now let's dive through below concepts/criteria to help gaining confidence on building your ML model: Activation Functions (Forward Propaga...
Post a Comment
Read more

(AI #2) Building a Real-World Neural Network: A Practical Use Case Explained

This blog will explain a clear picture on what will happen inside a Neural Network(NN).  But before going through NN, we need to have some knowledge on some of the basic concepts in Calculus(Maths) & architecture of a Neural Network.  Note :   I recommend you to read the following blog(link mentioned below) and then start reading this blog. Previous blog link :  https://arunsdatasphere.blogspot.com/2026/01/deep-learning-and-neural-networks.html   At-least try to  understand the basic layers of NN, weights, biases, activation function, loss function etc. Lets start with Derivatives. Derivatives :                      Derivatives are originally a core concept of calculus (maths) . They answer one question which is  “How fast is something changing?”  Why derivatives appear in Machine Learning ? Machine Learning uses Math as its foundation. In ML, derivatives help answer : If I sligh...
Post a Comment
Read more