Amazon Web Services also known as AWS is the world’s leading cloud services provider that enables enterprises and startups to build, deliver, and operate applications through cloud computing.
With a vast number of services under its umbrella, AWS continues to impress its customers by releasing new products that make it easier for them to develop, deploy, and manage applications in the cloud.
With the ever-increasing data volumes being produced by businesses every day across various business functions, it can be challenging to find actionable insights from raw data. Luckily, Amazon has introduced a new service called AWS Glue (previously known as Amazon Data Catalog) that streamlines your organization’s unstructured data storage in one centralized location.
Well, get ready to meet AWS Glue.
- What is AWS Glue?
- AWS Glue Pricing?
- Why and When to use AWS Glue?
- Key Features of AWS Glue
- How does AWS Glue work?
- Pros & Cons of AWS Glue?
- Key takeaways
What is AWS Glue?
AWS Glue is a serverless data integration and ETL service. This service makes it easy to prepare data for analytics, machine learning, and application development. It provides all the capabilities needed for data integration to gain insights and put data to use in minutes instead of months.
It is designed to discover value from data through one centralized location. Easily integrating with other AWS data services such as S3, Lambda, and others.
No infrastructure to set up or manage. Pay only for the resources consumed while your jobs are running.
AWS Glue is mostly using by Data engineers and ETL developers to create, run and monitor ETL workflows.
It has three major components: AWS Glue Data Catalog, ETL engine creating Python or Scala code automatically, and configurable scheduler.
AWS Glue Pricing?
Amazon Glue pricing varies by region and differs with the type of data process.
<strong>FUN FACT: North America currently have 7 regions 1. Oregon (launched in 2011), 2. Northern California (launched in 2009), 3. AWS GovCloud (US-West) (launched in 2011), 4. AWS GovCloud (US-East) (launched in 2018), 5. Ohio (launched in 2016), 6. Northern Virginia (launched in 2006), 7. Canada Central</strong> <strong>(launched in 2016)</strong> .
For Crawlers (discovering data) and ETL jobs (processing and loading data): hourly rate, billed by the second.
For the Amazon Glue Data Catalog: simple monthly fee for storing and accessing the metadata.
If you provision a development endpoint to interactively develop your ETL code, you pay an hourly rate, billed per second.
Example: Pricing for Northern California Region:
- $0.44 per DPU-Hour for each Apache Spark or Spark Streaming job, billed per second with a 1-minute minimum (Glue version 2.0 and later) or 10-minute minimum (Glue version 0.9/1.0)
- $0.44 per DPU-Hour for each Python Shell job, billed per second, with a 1-minute minimum
- $0.44 per DPU-Hour for each provisioned Development Endpoint, billed per second with a 10-minute minimum
- $0.44 per DPU-Hour for each Interactive Session billed per second with a 1-minute minimum. AWS Glue Studio Job Notebooks are a built-in interface for Interactive Sessions and offered at no additional cost.
- $0.44 per DPU-Hour for each AWS Glue Studio data preview session, billed in 30-minute units and invoiced as development endpoints
Why and when to use AWS Glue?
Amazon Web Services has become synonymous with cloud computing, and its data services have played a major role in making this happen.
- To run serverless queries across Amazon S3 data lake. AWS Glue helps you get started right way by getting all the data available at single interface for analysis without needing to relocate.
- To comprehend your Data Assets. Data catalog makes job easy to find different AWS data sets. Also saves data in various AWS Services
- By building event driven ETL workflows, You can execute ETL operations once the data is available in Amazon S3 by calling the Glue ETL task from AWS Lambda service.
- Useful to organize, clean, verify, and format data in preparation for storage in a data warehouse or data lake.
Key Features of AWS Glue?
– Scalable Architecture – AWS Glue has been built with a highly scalable architecture that lets you store and process massive amounts of data. It automatically scales based on a load of data being processed.
– Automated ETL Process – AWS Glue has been engineered to automate data transformations with the help of a feature called the Automated ETL (Extract, Transform, Load) process. This feature helps you automate your data extraction, transformation, and loading process thereby saving you the time and effort of doing it manually.
– Consistent Data Quality – Data quality is the key to any successful data analytics initiative.
AWS Glue comes with features that lets you clean and transform your data by using standard SQL syntax. It also uses built-in algorithms to make sure that the data being processed is consistent. – Metadata Driven –
AWS Glue lets you manage your data using metadata. This lets you automate your data ingestion, transformation, and loading processes using standard SQL that makes it easier for you to work with your data using a centralized dashboard. – Easy to Deploy – AWS Glue has been designed to be deployed in any modern data architecture. It can be deployed in any public cloud environment or on-premises data center.
How does AWS Glue work?
As it gives the flexibility to scale based on your business needs by integrating with other AWS data services such as S3, Lambda, and others.
This data can be in the form of images, PDFs, word documents and many more. The data gets automatically converted into a structured format which makes it easier for you to analyze and gain insights from it.
AWS Glue facilitates the entire data transformation process by using automated ETL. This process allows you to run complex transformations such as cleaning, parsing, and enriching your data.
It gives you the ability to load your data into Amazon Redshift for analytics, or other data warehouses or data lakes. It also allows you to load data into Amazon S3, Amazon DynamoDB, Amazon Elasticsearch Service, and other data stores.
Pros & Cons of AWS Glue?
PROS:
- AWS Glue is mostly used for ETL (extract, transform, load) and analytics because it is designed to handle big data. It can help you to collect data from multiple sources and then store, transform, and load it into other systems like Amazon Redshift, Amazon S3, Amazon RDS, Amazon Elasticsearch Service, or Amazon Athena.
- For instance, if you have data stored in a distributed database, you can use AWS Glue to regularly retrieve this data and ship it to a centralized data lake where you can store it safely. This centralized data lake will allow you to use tools like Amazon Athena to run complex queries against your data thanks to the additional storage space it provides. You can also use AWS Glue to collect data from various sources like SaaS services like Salesforce and Dropbox.
CONS:
- AWS Glue is a very powerful tool, but as with all technologies it has its limitations that you need to be aware of before committing to it. The first thing to understand is that AWS Glue is not an ETL tool that you can use for simple extract and loads. Rather, it is a data automation tool that can help you to handle big data and distributed systems.
- Therefore, if you have a centralized database where all your data is stored, and you need to regularly load this data into another system, It will not work for you. It is designed to handle data from distributed systems where you have no control over the source system.
Key takeaways
- AWS Glue is a data service that has been designed to simplify the way businesses store, clean, and analyze data.
- It is a serverless data integration tool letting you store unstructured data in one centralized location.
- Mostly used by Data Engineers & ETL Developers to create, run and monitor ETL workflows.
- Designed to be easily integrated with other AWS data services such as S3, Lambda, and others.
- With the help of AWS Glue businesses can now fully leverage their data to uncover hidden insights and increase their ROI.
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