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In recent years, most companies handle big data, and they are finding ways to analyze and store the data to help them beat their competitors. Most of them now invest in cloud services that ensure a good data integration process. It makes them use ETL processes to analyze the data and make wise decisions. It gives the team the power to collect data from different sources like organization databases, customers data, e.t.c, and store them in one data lake. According to a New Vintage survey done in 2017, it suggests many companies are on the verge of using big data to innovate and find ways to make their brand successful. This article will cover a brief introduction of AWS glue, its components and features, the pricing, the advantages, and the limitation of the service
AWS Glue is a product by Amazon that helps carry out ETL jobs during data integration. Most of the companies that use AWS glue work with many ETL processes. It uses a code-based interface to make the processes faster. It's serverless, making it easier to combine and integrate data during machine learning, application development, and analytics.
ETL is a process that covers the extraction, transformation, and loading of data. Extraction involves collecting data from different sources like databases, transformation involves validating the data by checking several issues, and loading is the step where you now move the data from the source to the data lake or warehouse.
All you need is a well-organized ETL system to ensure you get a good data analysis. It increases accuracy and eliminates issues that can affect the transformation and handling of data. Most ETL processes use automated scripts.
AWS Glue consists of the following components:
AWS Glue catalog-It holds the structure and metadata of the information.
Developer endpoint-it provides an environment for testing, editing, and debugging job scripts.
Crawler and classifier-crawler use classifiers to get data from sources, while the classifier uses the metadata table.
Trigger-it happens when the ETL job is on demand.
Database- enables the user to access or create a database both for the target and source.
Features of AWS Glue
1. Discovery
It uses Datalog to store all the data using schemas, jobs, control information, and tables. It enables users to view any data changes within a short period and makes queries faster and cheaper.
It uses crawlers to connect to the data source, going through different classifiers to find the schema of the data and create metadata in the datalog. The metadata helps in running the ETL jobs.
Due to its serverless nature, it makes autoscaling easier by rationing resources according to the type of workload. This reduces the cost and wastage of resources.
2. Transformation
It provides a drag and drop editor where users get a place to generate code for the ETLjobs(extract, transform, load). Most of the code generated is in Scala or Python and mainly for Apache Spark.
It consists of ETL pipelines that help in job scheduling. It has made it possible to handle different job dependencies by removing data and fixing failed jobs. To get notified of issues, it provides logs and alerts accessible through the Amazon CloudWatch.
It has serverless streaming to help move data from other Amazon sources like Amazon Kinesis and transfer it to the target datastore. This feature makes it easier to produce good analytics and make other operations swift.
3. Preparation of data
It has Sensitive Data Detection that helps users deal with sensitive data in the data lake. It identifies the data by using personal information like email, name, license, ID number, e.t.c.After identification, the data gets masked.
It has Built-in Studio Job Notebooks that ensure that the serverless notebooks in the AWS Glue studio get set up faster and quickly. The studio provides a clean interface where developers can schedule and run any notebook code.
It has Interactive Sessions that enable data scientists to perform data integration tasks easily. It gives them platforms to explore and analyze according to the notebook they like.
It has DataBrew, which provides data scientists with an interface to clean project data without code. It works with all data sets from databases, data lakes, and warehouses. It offers over 200 built-in transformation templates that you can choose from to do data analysis tasks like transposing and combining data.
It has endpoints where you can debug and test code. It gives you the power to choose any notebook for writing readers and transformations and using them in your project as custom libraries.
It has a FindeMatches feature that helps users with machine learning jobs without machine learning skills to remove duplicates and non matching data, making analysis easier.
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4. Replication
It has elastic views that help create views that get stored in the data stores. You can achieve this with the help of queries using PartiQL (SQL type language). It makes it easier to manipulate the data and helps in communicating with the AWS console through an API.
It works well with Amazon DynamoDB and other Amazon services like Amazon S3, Amazon Aurora, Amazon RDS, Amazon Redshift e.t.c.With views, it's easier to monitor and update any changes in the respective data stores automatically.
There are different pricing options that AWS Glue uses. You have to pay using an hourly rate and get billed per second. Different regions can also have varying prices. Some of the AWS glue components have different features.AWS Glue Data Catalog offers the first free million access to stored objects.AWS Glue DataBrew component uses interactive sessions, which get priced per session. When using AWS Glue Schema components, there are no charges. Some of the pricing options include:
ETL jobs.Some of the charges in these categories include:$0.44 per DPU per hour for Apache Spark,$0.44 for Python Shell,$0.44 for Interactive sessions, and $0.44 for Development Endpoint. This is for the US East location, and each location has different prices.
Data Catalog Storage. They charge $1.00 for every 100,000 objects per month. It also charges the same amount for one million requests per month.
Crawlers. For this category, AWS Glue charges $0.44 hourly per DPU. The minimum they charge is using 10 minutes per crawl.
Databrew Interactive sessions. A session gets counted once data gets loaded. They offer a free plan for the first 40 sessions. They charge $1.00 per session.
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Databrew jobs. In this category, you only pay when they calculate the time you use to clean the data when running the jobs. They charge $0.48 hourly per Databrew node.
Elastic views. They usually help developers build views and later combine them with the data stores without writing code. It makes it easier to use SQL and move data from one source to the target data storage place. They charge $0.16 hourly per view processing.
AWS glue has been beneficial since its invention in helping data integration tasks. Some of the benefits of using AWS Glue include:
Despite having a lot of benefits, it also has some limitations. Some of the disadvantages include:
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Since the invention of AWS Glue, many companies have embraced the technology by integrating it into their tools. They use it to generate reports and analysis that breaks down expected action. It helps in achieving all these with short periods and less staff. Most of the processes are automated, making it unnecessary to have many people in your team to carry out the tasks.
AWS Glue performs updates on the service after a certain period. Recently, they added new features like Python version 3.6, support for CSV classifier, and logging for Apache Spark jobs, among others. It's a service under improvement, and Amazon will roll out new features will be rolled out to improve its functionality.
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