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. While working with this complex 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 the 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.
Table of Contents
- Introducing to ETL Tools
- Advantages of using an ETL Tool
- What is AWS Glue Data Catalog?
- Applications of AWS Glue Data Catalog
- What are the components of AWS Glue Data Catalog?
- Benefits and Limitations of AWS Glue Data Catalog
- AWS Glue vs EMR: Key Differences
- Measuring and Monitoring AWS Glue Costs
- How to get Metadata into the AWS Glue Data Catalog?
- What Analytics Services use the AWS Glue Data Catalog?
- Steps to Add Metadata Tables to AWS Glue Data Catalog
Introduction to ETL Tools
ETL stands for Extract, Transform, Load. The name is self-explanatory. This tool helps in extracting the necessary data from a source, make any transformations to the data if needed, and load it in the destination. The destination need not necessarily be a Data Warehouse. It can be anything from a Database, Excel Sheet, CRM software, BI tool, etc. But ETL tools are predominantly used for moving data into Data Warehouse because this is where all the analysis happens.
There are many ETL tools like Hevo Data, AWS Glue Data, Fivetran, Stitch, etc that can help you move your data. Each of them has its own unique characteristics. You can choose a tool that suits your needs the best. ETL tools are used by organizations to work with Sales and Marketing data, Big Data, data for analytics, etc. ETL tools come in handy whenever you have a regular need to move data from one place to another.
Advantages of using an ETL Tool
Now that you know what an ETL tool is, let’s talk about the advantages of using an ETL tool:
- Near Real-Time Data Transfer
- Fully Managed
- Makes Source and Destination Data Compatible
- Saves Time
- Secures Data
1) Near Real-Time Data Transfer
Often in an organization, data is required to be moved on a regular basis. For example, the Sales and Marketing data keep changing with time. This change of data needs to be reflected at all the places and teams dealing with this data. Imagine doing all this manually! It’s tedious and takes a lot of configuring. ETL tools come to your rescue because once you configure an ETL tool for a source and destination, any change in data in the source reflects in the destination. It’s called near-real-time data transfer because any tool has some amount of latency but it is generally not significant as to become an issue.
2) Fully Managed
ETL software is fully managed by the software providers. This means you need not worry about your ETL tool going out of date. Your ETL provider will keep you informed of all the updates and will manage the whole ETL software. This also includes implementing fault-tolerant mechanisms, estimating the load on source and destination, maintaining proper data formats, etc.
3) Makes Source and Destination Data Compatible
Sometimes the source data cannot be directly moved into the destination. This is because before moving to the destination an ETL tool needs to identify which data goes into which place in the destination and if so you need to know that the data formats are compatible. All these changes are made during the transformation phase and then the data is loaded into the destination.
4) Saves Time
As simple as the ETL concept sounds, in reality, it is difficult to build and maintain an ETL tool manually. It will require lots of effort, resources, and time. You can save an amazing amount of time if you can outsource all this work to an ETL tool
5) Secures Data
There is a good chance that ETL tools deal with your organization’s sensitive data. So ETL tools follow a lot of security protocols and get themselves certified. They encrypt your data and keep it secure from any kind of threats or breaches.
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What is AWS Glue Data Catalog?
An ETL tool deals with a lot of data. It has information about the source and destination, keeps a tab on the data being transferred, on 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, 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:
- Amazon S3
Crawlers can crawl the following data stores via a JDBC connection:
- Amazon Redshift
- Amazon Relational Database Service (Amazon RDS)
- Amazon Aurora
- Microsoft SQL Server
- Publicly accessible databases (on-premises or on another cloud provider environment)
- Microsoft SQL Server
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:
- 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.
Benefits and Limitations of AWS Glue Data Catalog
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 schedule, 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.
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.
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.
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 it 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, Amazon EMR bills you about $14-$16 for a similar configuration.
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 spend. You can also see exactly how services drive your cloud costs and why.
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 structure 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
Steps to Add Metadata Tables to AWS Glue Data Catalog
Now you will learn about adding metadata tables to AWS Glue Data Catalog :
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
- Step 2: Defining the Database in AWS Glue Data Catalog
- Step 3: Defining Tables in AWS Glue Data Catalog
- Step 4: Defining Crawlers in AWS Glue Data Catalog
- Step 5: Adding Tables in AWS Glue Data Catalog
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, 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.
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.
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