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This submit is co-written with Hemant Aggarwal and Naveen Kambhoji from Kaplan.
Kaplan, Inc. gives people, academic establishments, and companies with a broad array of companies, supporting our college students and companions to fulfill their numerous and evolving wants all through their academic {and professional} journeys. Our Kaplan tradition empowers folks to attain their targets. Dedicated to fostering a studying tradition, Kaplan is altering the face of training.
Kaplan information engineers empower information analytics utilizing Amazon Redshift and Tableau. The infrastructure gives an analytics expertise to tons of of in-house analysts, information scientists, and student-facing frontend specialists. The info engineering crew is on a mission to modernize its information integration platform to be agile, adaptive, and easy to make use of. To realize this, they selected the AWS Cloud and its companies. There are numerous sorts of pipelines that must be migrated from the prevailing integration platform to the AWS Cloud, and the pipelines have various kinds of sources like Oracle, Microsoft SQL Server, MongoDB, Amazon DocumentDB (with MongoDB compatibility), APIs, software program as a service (SaaS) purposes, and Google Sheets. When it comes to scale, on the time of writing over 250 objects are being pulled from three completely different Salesforce cases.
On this submit, we focus on how the Kaplan information engineering crew applied information integration from the Salesforce utility to Amazon Redshift. The answer makes use of Amazon Easy Storage Service as a knowledge lake, Amazon Redshift as a knowledge warehouse, Amazon Managed Workflows for Apache Airflow (Amazon MWAA) as an orchestrator, and Tableau because the presentation layer.
Resolution overview
The high-level information circulate begins with the supply information saved in Amazon S3 after which built-in into Amazon Redshift utilizing varied AWS companies. The next diagram illustrates this structure.
Amazon MWAA is our primary device for information pipeline orchestration and is built-in with different instruments for information migration. Whereas looking for a device emigrate information from a SaaS utility like Salesforce to Amazon Redshift, we got here throughout Amazon AppFlow. After some analysis, we discovered Amazon AppFlow to be well-suited for our requirement to drag information from Salesforce. Amazon AppFlow gives the power to straight migrate information from Salesforce to Amazon Redshift. Nonetheless, in our structure, we selected to separate the information ingestion and storage processes for the next causes:
- We would have liked to retailer information in Amazon S3 (information lake) as an archive and a centralized location for our information infrastructure.
- From a future perspective, there may be eventualities the place we have to rework the information earlier than storing it in Amazon Redshift. By storing the information in Amazon S3 as an intermediate step, we are able to combine transformation logic as a separate module with out impacting the general information circulate considerably.
- Apache Airflow is the central level in our information infrastructure, and different pipelines are being constructed utilizing varied instruments like AWS Glue. Amazon AppFlow is one a part of our general infrastructure, and we needed to keep up a constant strategy throughout completely different information sources and targets.
To accommodate these necessities, we divided the pipeline into two elements:
- Migrate information from Salesforce to Amazon S3 utilizing Amazon AppFlow
- Load information from Amazon S3 to Amazon Redshift utilizing Amazon MWAA
This strategy permits us to make the most of the strengths of every service whereas sustaining flexibility and scalability in our information infrastructure. Amazon AppFlow can deal with the primary a part of the pipeline with out the necessity for every other device, as a result of Amazon AppFlow gives functionalities like making a connection to supply and goal, scheduling the information circulate, and creating filters, and we are able to select the kind of circulate (incremental and full load). With this, we had been in a position to migrate the information from Salesforce to an S3 bucket. Afterwards, we created a DAG in Amazon MWAA that runs an Amazon Redshift COPY command on the information saved in Amazon S3 and strikes the information into Amazon Redshift.
We confronted the next challenges with this strategy:
- To do incremental information, we now have to manually change the filter dates within the Amazon AppFlow flows, which isn’t elegant. We needed to automate that date filter change.
- Each elements of the pipeline weren’t in sync as a result of there was no method to know if the primary a part of the pipeline was full in order that the second a part of the pipeline might begin. We needed to automate these steps as effectively.
Implementing the answer
To automate and resolve the aforementioned challenges, we used Amazon MWAA. We created a DAG that acts because the management heart for Amazon AppFlow. We developed an Airflow operator that may carry out varied Amazon AppFlow capabilities utilizing Amazon AppFlow APIs like creating, updating, deleting, and beginning flows, and this operator is used within the DAG. Amazon AppFlow shops the connection information in an AWS Secrets and techniques Supervisor managed secret with the prefix appflow. The price of storing the key is included with the cost for Amazon AppFlow. With this, we had been in a position to run the whole information circulate utilizing a single DAG.
The entire information circulate consists of the next steps:
- Create the circulate within the Amazon AppFlow utilizing a DAG.
- Replace the circulate with the brand new filter dates utilizing the DAG.
- After updating the circulate, the DAG begins the circulate.
- The DAG waits for the circulate full by checking the circulate’s standing repeatedly.
- A hit standing signifies that the information has been migrated from Salesforce to Amazon S3.
- After the information circulate is full, the DAG calls the COPY command to repeat information from Amazon S3 to Amazon Redshift.
This strategy helped us resolve the aforementioned points, and the information pipelines have change into extra sturdy, easy to know, simple to make use of with no handbook intervention, and fewer vulnerable to error as a result of we’re controlling the whole lot from a single level (Amazon MWAA). Amazon AppFlow, Amazon S3, and Amazon Redshift are all configured to make use of encryption to guard the information. We additionally carried out logging and monitoring, and applied auditing mechanisms to trace the information circulate and entry utilizing AWS CloudTrail and Amazon CloudWatch. The next determine reveals a high-level diagram of the ultimate strategy we took.
Conclusion
On this submit, we shared how Kaplan’s information engineering crew efficiently applied a sturdy and automatic information integration pipeline from Salesforce to Amazon Redshift, utilizing AWS companies like Amazon AppFlow, Amazon S3, Amazon Redshift, and Amazon MWAA. By making a customized Airflow operator to manage Amazon AppFlow functionalities, we orchestrated your complete information circulate seamlessly inside a single DAG. This strategy has not solely resolved the challenges of incremental information loading and synchronization between completely different pipeline phases, however has additionally made the information pipelines extra resilient, simple to keep up, and fewer error-prone. We lowered the time for making a pipeline for a brand new object from an current occasion and a brand new pipeline for a brand new supply by 50%. This additionally helped take away the complexity of utilizing a delta column to get the incremental information, which additionally helped scale back the price per desk by 80–90% in comparison with a full load of objects each time.
With this contemporary information integration platform in place, Kaplan is well-positioned to supply its analysts, information scientists, and student-facing groups with well timed and dependable information, empowering them to drive knowledgeable choices and foster a tradition of studying and progress.
Check out Airflow with Amazon MWAA and different enhancements to enhance your information orchestration pipelines.
For extra particulars and code examples of Amazon MWAA, check with the Amazon MWAA Consumer Information and the Amazon MWAA examples GitHub repo.
Concerning the Authors
Hemant Aggarwal is a senior Knowledge Engineer at Kaplan India Pvt Ltd, serving to in creating and managing ETL pipelines leveraging AWS and course of/technique improvement for the crew.
Naveen Kambhoji is a Senior Supervisor at Kaplan Inc. He works with Knowledge Engineers at Kaplan for constructing information lakes utilizing AWS Companies. He’s the facilitator for your complete migration course of. His ardour is constructing scalable distributed programs for effectively managing information on cloud.Outdoors work, he enjoys travelling together with his household and exploring new locations.
Jimy Matthews is an AWS Options Architect, with experience in AI/ML tech. Jimy relies out of Boston and works with enterprise clients as they rework their enterprise by adopting the cloud and helps them construct environment friendly and sustainable options. He’s enthusiastic about his household, automobiles and Blended martial arts.
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