Working with different types of data has become essential in the Big Data era because analysis of this data gives you valuable information that enables you to make better data-driven decisions for your business. When working with this complex set of business data, you’ll need to unify and load this data into your desired destination at some point.
While some data transfers may be easy others can be challenging due to the large volumes of data, along with source & destination incompatibility. To run these tasks smoothly, Data Warehouses need some central repository like AWS Glue Data Catalog, Alation, etc. for maintaining metadata and ETL jobs.
To make these tasks easier, ETL tools have come into the picture. These tools help in moving data from one place to another. AWS Glue Data is a software developed by Amazon that keeps a tab of all the metadata related to an ETL tool. In this article, we will learn about ETL tools, their advantages, and AWS Glue Data Catalog.
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Hevo takes care of your complex ETL processes and allows you to focus on key business needs and data analysis using a BI tool of your choice.
What is AWS Glue Data Catalog?
Image Source: Amazon
An ETL tool deals with a lot of data. It has information about the source and destination and keeps a tab on the data being transferred, the underlying mechanisms, system failures, etc. For all this, an ETL tool needs to store all the required metadata. Amazon AWS Glue Data Catalog is one such Sata Catalog that stores all the metadata related to the AWS ETL software.
AWS Glue Data Catalog tracks runtime metrics, and stores the indexes, locations of data, schemas, etc. It basically keeps track of all the ETL jobs being performed on AWS Glue. All this metadata is stored in the form of tables where each table represents a different data store.
Applications of AWS Glue Data Catalog
Before diving into this concept you need to understand why you need the AWS Glue Data Catalog :
- Keeps you informed because all the data related to ETL processes is stored.
- Error tracing becomes easier because you can look back into what went wrong and why.
- Easy to ensure fault tolerance because it regularly keeps tabs on the data.
- No need to set up configurations again as it stores all the configurations and connections.
If it were not for AWS Glue Data Catalog, you would need to manually keep a tab of data and this data needs very regular monitoring which can be a tremendously challenging task.
What are the Components of AWS Glue Data Catalog?
The AWS Glue Data Catalog consists of the following components:
1) Databases and Tables
Databases and Tables make up the Data Catalog. A Table can only exist in one Database. Your Database can contain Tables from any of the AWS Glue-supported sources.
2) Crawlers and Classifiers
A Crawler assists in the creation and updating of Data Catalog Tables. It has the ability to crawl both file-based and table-based data stores.
Crawlers can crawl the following data stores via their native interfaces:
Crawlers can crawl the following data stores via a JDBC connection:
- Amazon Redshift
- Amazon Relational Database Service (Amazon RDS)
- Amazon Aurora
- Microsoft SQL Server
- MySQL
- Oracle
- PostgreSQL
- Publicly accessible databases (on-premises or on another cloud provider environment)
- Aurora
- Microsoft SQL Server
- MySQL
- Oracle
- PostgreSQL
A Classifier in the AWS Glue crawler recognizes the data format and generates the schema. AWS Glue comes with a set of built-in classifiers, but you can also create your own Custom Classifiers.
3) Connections
Connections allow you to centralize connection information such as login credentials and virtual private cloud (VPC) IDs. This saves time because you don’t have to input connection information each time you create a crawler or job.
The following Connection types are available:
- JDBC
- Amazon RDS
- Amazon Redshift
- MongoDB, including Amazon DocumentDB (with MongoDB compatibility)
- Network (designates a connection to a data source within a VPC environment on AWS)
4) AWS Glue Schema Registry
The AWS Glue Schema Registry allows disparate systems to share a serialization and deserialization schema. Assume you have a data producer and a data consumer, for example. Whenever the serialized data is published, the producer is aware of the schema. The consumer makes use of the Schema Registry deserializer library, which extracts the schema version ID from the record payload. The schema is then used by the consumer to deserialize the data.
With the AWS Glue Schema Registry, you can manage and enforce schemas on your data streaming applications using convenient integrations with the following data input sources:
- Apache Kafka
- Amazon Managed Streaming for Apache Kafka
- Amazon Kinesis Data Streams
- Amazon Kinesis Data Analytics for Apache Flink
- AWS Lambda
Schema Registry consists of the following components:
- Schemas: A Schema is a representation of the structure and format of a data record.
- Registry: A Registry is a logical container for schemas. You can use registries to organize your schemas and manage access control for your applications.
Hevo Data Pipelines take away the tedious task of schema management and automatically detects the schema of incoming data to map it to your destination schema.
Benefits and Limitations of AWS Glue Data Catalog
Benefits
Here are the benefits of leveraging AWS Glue Data Catalog:
- Increased Data Visibility: AWS Glue Data Catalog helps you monitor all your data assets by acting as the metadata repository for information on your data sources and stores.
- Automatic ETL Code: AWS Glue can automatically generate ETL Pipeline code in Scala or Python- based on your data sources and destination. This helps you streamline the data integration operations and parallelize heavy workloads.
- Job Scheduling: AWS Glue provides simple tools for creating and following up job tasks based on event triggers and schedules, or perhaps on-demand.
- Pay-as-you-go: AWS Glue doesn’t force you to commit to long-term subscription plans. Instead, you can minimize your usage costs by opting to pay only when you need to use it.
- Serverless: AWS Glue helps you save the effort and time required to build and maintain infrastructure by being a serverless Data Integration service. Amazon provides and manages the servers in AWS Glue.
Limitations
Here are the disadvantages of using AWS Glue:
- Limited Integrations: AWS Glue is only built to work with other AWS services. This means that you won’t be able to integrate it with platforms outside the Amazon ecosystem.
- Requires Technical Knowledge: A few aspects of AWS Glue are not very friendly to non-technical beginners. For example, since all the tasks are run in Apache Spark, you need to be well-versed in Spark to tweak the generated ETL jobs. Apart from this, the ETL code itself can only be worked on by developers who understand Scala or Python.
- Limited Support: When it comes to customizing ETL codes, AWS Glue provides support for only two programming languages: Scala and Python.
How to Get Metadata Into the AWS Glue Data Catalog?
AWS Glue provides a number of ways to populate metadata into the AWS Glue Data Catalog.
- Glue crawlers scan your data stores to infer schemas and partition structures and populate the Glue Data Catalog with corresponding table definitions and statistics.
- Crawlers can also be scheduled to run on a regular basis to ensure that your metadata is always up to date and in sync with the underlying data.
- You can also manually add and update table details using the AWS Glue Console or the API call method.
- Hive DDL statements can also be executed on an Amazon EMR cluster using the Amazon Athena Console or a Hive client.
- Finally, if you already have a persistent Apache Hive Metastore, you can use the import script to bulk import that metadata into the AWS Glue Data Catalog.
What Analytics Services Use the AWS Glue Data Catalog?
The metadata stored in the AWS Glue Data Catalog is easily accessible via:
- Glue ETL
- Amazon Athena
- Amazon EMR
- Amazon Redshift Spectrum, and
- Third-party services
These are some other benefits of having Hevo Data as to your Data Automation Partner:
- Exceptional Security: A Fault-tolerant Architecture that ensures Zero Data Loss.
- Built to Scale: Exceptional Horizontal Scalability with Minimal Latency for Modern-data Needs.
- Built-in Connectors: Support for 150+ Data Sources, including Databases, SaaS Platforms, Files & More. Native Webhooks & REST API Connector available for Custom Sources.
- Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility is designed for everyone.
- Smooth Schema Mapping: Fully-managed Automated Schema Management for incoming data with the desired destination.
- Blazing-fast Setup: Straightforward interface for new customers to work on, with minimal setup time.
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Steps to Add Metadata Tables to AWS Glue Data Catalog
Image Source: Amazon
Now you will learn about adding metadata tables to AWS Glue Data Catalog:
Image Source: Self
Sign in to your AWS account and select AWS Glue Console from the management console and follow the below-given steps:
Step 1: Defining Connections in AWS Glue Data Catalog
Creating connections helps you store the login credentials, URI string, and connection information for a particular data store (source or target). By creating connections you don’t have to configure this every time you run a Crawler. Go to Connections in AWS Glue Console. In the connection wizard, specify the connection name, and connection type and choose whether you require an SSL connection.
Step 2: Defining the Database in AWS Glue Data Catalog
First, define a database in your AWS Glue Catalog. Select the Databases tab from the Glue Data console. In this Database tab, you can create a new database by clicking on Add Database. In the window that opens up, type the name of the database and its description. You can also edit an existing one with the Edit option and delete a database using the Delete option in the Action tab.
Step 3: Defining Tables in AWS Glue Data Catalog
A single table in the AWS Glue Data Catalog can belong only to one database. To add a table to your AWS Glue Data Catalog, choose the Tables tab in your Glue Data console. In that choose Add Tables using a Crawler. Now an Add Crawler wizard pops up.
Step 4: Defining Crawlers in AWS Glue Data Catalog
Before defining the Crawlers there are prerequisites you must implement first. You have to set the Identity and Access Management configurations first. Learn more about this here.
Now choose Crawlers in the AWS Glue Catalog Console. Choose Add Crawler. A Crawler wizard will take you through the remaining steps.
Step 5: Adding Tables in AWS Glue Data Catalog
After you define a Crawler, you can run the Crawler. If the Crawler runs successfully it creates metadata table definitions for your AWS Glue Data Catalog.
Measuring and Monitoring AWS Glue Costs
AWS Glue’s pay-as-you-go rate of $0.44/DPU might look like a reasonable bet, however, organizations commonly find themselves with bloated bills after prolonged use. This can run into thousands of dollars per month one extra or unnecessary cost.
These cost overruns are primarily due to poor AWS Cost Management practices. Since Amazon doesn’t readily provide comprehensive insights, a task as simple as keeping tabs on your AWS Glue spend can be a challenge.
You can use Cloud Cost Intelligence to drill into cost data from a high level down to the individual components that drive your cloud spending. You can also see exactly how services drive your cloud costs and why.
AWS Glue vs EMR: Key Differences
Apart from AWS Glue, you also have AWS Kinesis, AWS EMR, AWS Athena, AWS Redshift, and AWS Data Exchange that are capable of handling Big Data.
At first glance, it may be difficult to differentiate between AWS Glue and AWS EMR since these two services share considerable similarities.
AWS Glue exists as a serverless ETL system that offers its services on a pay-as-you-go basis. You can rely on AWS Glue to automate the bulk of the tasks in monitoring, writing, and executing ETL jobs. You can also read our AWS Glue tutorial to get more ideas about it.
AWS EMR or AWS Elastic MapReduce, on the other hand, is known for reducing the cost of processing and analyzing huge volumes of data through a managed Big Data platform. Instead of restricting your configuration options, it lets you set up custom EC2 (Amazon Elastic Computing) instance clusters, and create Hadoop ecosystem elements.
However, AWS EMR requires you to have your own extensive infrastructure if you want to leverage it for Big Data operations. This makes getting started a costly affair. But once you set up the infrastructure, you will have an easy time deploying AWS EMR- plus capitalizing on its flexibility and power which gives it an edge over AWS Glue. Data Analysts can use AWS EMR to perform SQL queries on Presto.
In terms of costs, AWS Glue charges you around $21/DPU (Data Processing Unit) for an entire day, and Amazon EMR bills you about $14-$16 for a similar configuration.
AWS Glue vs Hevo Data
You can get a better understanding of Hevo’s Data Pipeline as compared to AWS Glue using the following table:
| S.no | Parameter | AWS Glue | Hevo Data |
| 1) | Specialization | ETL, Data catalog | ETL, Data Replication,
Data Ingestion |
| 2) | Pricing | AWS Data Catalog charges monthly for storage while AWS Glue ETL charges on per hour basis. | Hevo follows a flexible & transparent pricing model where you pay as you grow. Hevo offers 3 tiers of pricing, Free, Starter & Business. Check out the details here.
|
| 3) | Data Replication | Full table; Incremental via Change Data Capture (CDC) through AWS Database Migration Service (DMS). | Full table; Incremental via SELECT/Replication key, Timestamp & Change Data Capture (CDC). |
| 4) | Connector Availability | AWS Glue caters to Amazon platforms such as Redshift, S3, RDS, and DynamoDB — and AWS destinations, and other databases via JDBC | Hevo has native connectors with 100+ data sources and integrates with Redshift, BigQuery, Snowflake, and other Data Warehouses & BI tools. Check out the complete integrations list here. |
Hevo Data, compared to AWS Glue is a better alternative since you get to have features like Data Extraction, Data Transformation, Data Replication, and Data Analysis (through the use of Business Intelligence tools), using a consistent, single, and user-friendly interface.
Hevo Pipelines can ingest data from a wide variety of 150+ Data Sources (including 40+ free sources) such as Databases, SaaS applications, Cloud Storage, SDKs, Streaming Services, and many more. Choosing to use Hevo will not only provide your teams with a fault-tolerant and scalable architecture, but you also get to avail yourself of the power of having ready-to-be-analyzed data (in minutes), using Hevo’s in-built data formatting and transformation capabilities that can be queried to extract meaningful insights.
Conclusion
In this article, you learned about ETL tools, their benefits, and also how AWS Glue Data Catalog helps in storing the metadata and working with it. Why create all these configurations and connections when you can use an ETL tool that manages its metadata on its own.
Hevo Data is an all-in-one cloud-based ETL pipeline that will not only help you transfer data but also transform it into an analysis-ready form. Hevo’s native integration with 100+ sources (including 40+ free sources) ensures you can move your data without the need to write complex ETL scripts. Hevo’s automated data transfer, data source connectors, and pre-post transformations are advanced compared to Apache airflow. It will make your life easier and make data migration hassle-free.
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Share your experience of working with AWS Glue Data Catalog in the comments section below!
Easha is a programming enthusiast having experience in automated test script creation, regression testing, and integration projects like Thyrocare Integration. She is keenly interested in creating technical articles related to data science and integration.