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One of the biggest challenges in data analytics is managing and transforming large amounts of data across multiple sources. This is where AWS Glue comes in. AWS Glue is a fully managed extract, transform, and load (ETL) service that simplifies the process of moving data between data stores. In this comprehensive guide, we will take a deep dive into AWS Glue and explore its key features, benefits, and use cases. We will also compare it with traditional ETL tools to illustrate its advantages. If you’re curious about AWS Glue and want to learn about its functionalities and capabilities, read on.<\/p>\n

What is ETL?<\/h2>\n

\"What
\nETL or Extract, Transform, Load is a process used in data integration to extract data from various sources, transform the data into a structure that’s suitable for analysis, and load the transformed data into a target destination. This process is crucial in modern data management and analysis, where data is spread across multiple sources and requires complex transformations to be usable. In the following sections, we’ll dive deeper into each step of ETL and explore how Amazon Web Services’ AWS Glue simplifies this process. So, let’s get started without any delay!<\/p>\n

Extract<\/h3>\n

During the ETL process, the first step is “Extract”, which is the process of extracting data from various sources such as databases, flat files, and other data sources. The data sources can be on-premises or in the cloud. The data is extracted in the form of structured, semi-structured, or unstructured data. The extracted data can be in different formats, such as CSV, JSON, Parquet, Avro, and more. <\/p>\n

Extract Process Steps:<\/strong><\/p>\n\n\n\n\n\n\n
Step<\/th>\nDescription<\/th>\n<\/tr>\n
Determine the data source<\/td>\nIn this step, you need to determine the data sources that you want to extract data from. These sources can be flat files, databases, or other sources.<\/td>\n<\/tr>\n
Connect to the data source<\/td>\nYou need to establish a connection to the data source to fetch the data. You can establish a connection using different protocols and APIs.<\/td>\n<\/tr>\n
Extract data<\/td>\nAfter connecting to the data source, you can extract the data based on some predefined criteria such as SQL query or NoSQL query, data filters, or other criteria.<\/td>\n<\/tr>\n
Store data<\/td>\nOnce you have extracted the data, the data needs to be stored in a staging area so that it can be processed in the next step.<\/td>\n<\/tr>\n<\/table>\n

AWS Glue provides four ways to extract data from different sources: Glue ETL jobs, Glue crawlers, AWS Database Migration Service, and AWS Snowball. Glue ETL jobs provide the flexibility to extract data from different sources and transform the data according to the business requirement.<\/p>\n

Glue crawlers help extract metadata from the source data to create a data catalog that can be used for mapping data to the target destination. AWS Database Migration Service is used for extracting data from on-premises data sources and migrating that data to the AWS cloud. AWS Snowball is a petabyte-scale data transport solution that helps to get data into and out of AWS.<\/p>\n

Now let’s move on to the next step of the ETL process which is “Transform”.<\/strong> For more information on AWS Glue, please refer to this article.<\/a><\/p>\n

Transform<\/h3>\n

During the ETL process, the next step after extraction is transformation. This step involves manipulating the extracted data to make it suitable for the target data store or data warehouse. It mainly includes data cleaning, data mapping, data conversion, filtering, and aggregation. Transformation is a crucial step since it cleans up any inconsistencies in the data, resolves redundant data, and updates the data to the required schema. There are various transformation types<\/strong> that can be applied to the data, including:<\/p>\n