Hello,
To build the pipeline, I use the following approach:
1) Data analysis: First, I will analyze the datasets to understand their size, format, and quality. I will also validate the data to ensure that it's complete, accurate, and in the expected format.
2) Data cleaning and filtering: Based on the analysis, I will develop several data cleaning and filtering functions using Spark dataframes to transform the data as per your requirements. I will also handle exceptions and errors gracefully to avoid data loss by applying appropriate DQ checks as per your requirement
3) Jar or Wheel file creation: After development of code, I will create a Jar or Wheel file as per your requirements.
4) Job scheduling: Once the Jar or Wheel file is created, I will schedule the job or run the job using Databricks Cluster or using Airflow as per your preference.
5) Testing: Before delivering the solution, I will test the pipeline thoroughly to ensure that it meets your requirements and performs as expected.
6) Documentation: Finally, I will create a documentation file that explains the pipeline's functionality and how to use it.
I am well-versed in the following technologies:
Big Data: Spark, Hadoop
Cloud Technologies: AWS, Databricks, Airflow
Data Storage: S3, Data Lake, AWS Kinesis Streams, Redshift, HDFS
Data Processing: Spark Dataframes, SQL
Programming Languages: Python, Scala
AWS Services: Lambda, Kinesis Stream, Athena, Glue, SNS, SQS, DynamoDB
Thanks and Regards
Krishnan