Automate Data Extraction with SQL and Sqoop on AWS

Check in this article how to transfer data from a relational database with limited permissions to a file system with unlimited permissions - Skoop.IO

Companies need to extract large amounts of data from different databases, transform and enrich them so that they generate added value for the business. 

The extraction process is usually complex to develop because, in some cases, the data provider only grants access to views and generally limited access to the database. This makes sense for security and best practices. Now, how do we extract data having limited access to the providers' database? What alternatives do we have? How do we develop the extraction so that it works regardless of the database engine? Keep reading.

Basic Concepts.

Before starting, we will review some concepts about big data and the tools Apache Sqoop, Apache Hadoop, Aws EMR, and Aws DynamoDB.

Big data: It is a term that describes the large volume of data, both structured and unstructured, that floods businesses every day. But it is not the amount of data that is important. What matters with Big Data is what organizations do with the data. Big Data can be analyzed to gain insights that lead to better decisions and strategic business moves. Source https://www.powerdata.es/big-data

Apache Sqoop:  It is a tool designed to efficiently transfer massive data between Apache Hadoop and structured data stores such as relational databases or vice versa. Source: https://sqoop.apache.org/

Apache Hadoop: It is an open-source framework that enables distributed storage and processing of large data sets based on commodity hardware. Within Apache Hadoop is HDFS (Hadoop File System) which is responsible for storing large amounts of data with horizontal scaling, using MapReduce for processing this data. Source: https://blog.powerdata.es/el-valor-de-la-gestion-de-datos/bid/397377/qu-es-el-apache-hadoop

AWS EMR: Amazon EMR is a managed cluster platform that simplifies running Big Data frameworks such as Apache Hadoop and Apache Spark on AWS to process and analyze large amounts of data. Source: https://docs.aws.amazon.com/es_es/emr/latest/ManagementGuide/emr-what-is-emr.html

AWS DynamoDB: It is a non-relational key-value database. Source: https://aws.amazon.com/es/dynamodb/

Now that we know these concepts generally, I will explain how we use these tools in a solution we call “Skoop-IO.”

There are different database extraction alternatives for big data, but the one that best fits our requirements is sqoop, thanks to it being a tool specialized in data extraction and also open source. We developed Skoop-IO based on the sqoop extraction tool.

Skoop-IO is a program capable of extracting records from different database engines, regardless of their limitations. The processing is automatic, and the only manual part is configuring the credentials and the type of processing (full, partial, or incremental).

Types of loads

Currently, Skoop-io has 3 types of loads, fully configurable for importing data to an S3 bucket in AWS.

Full: Retrieves all data from the database and places it in the S3 bucket in Avro format.

Partial: Retrieves all data using a date-type field from the last configured months.

Incremental: Retrieves records not existing in our storage in S3 on each query to skoop-io.

Architecture.

To build Skoop-io we used AWS resources, specifically EMR as a cluster for running the program, DynamoDB for data import configuration, and S3 for storing the imported data.

Thanks to Big Data tools and cloud resources (AWS in this case), we managed to quickly implement a stable solution for extracting relational data, independent of database engines or providers' limited access. 

Ready to optimize your data extraction processes?

At Kranio, we have the experience and necessary tools to help you implement efficient data extraction and processing solutions using technologies like Sqoop and AWS. Contact us and discover how we can drive the digital transformation of your company.