Aws Glue is a service provided by amazon for deploying ETL jobs. It decreases the cost and complexity, and time that we spend in making ETL Jobs. If any company is price sensitive and if needs many ETL use cases, Amazon Glue is the best choice.
1.Few Points to Consider about AWS Glue ETL:
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1. It is a Serverless service and no need for provisioning and managing services and Resources.
2. When glue runs actively; there is no need to pay for resources.
3. It comes with Crawlers that design metadata for viewing the data stored in S3. This metadata is easy when authoring the ETL Tools.
4. By using python scripts, the glue will translate one resource format, for one more format.
5. Here you design an end development by yourself. Every time this gives you the power to design your ETL Scripts in an easy and simple way.
2.Features of AWS Glue:
a)Simple Job Scheduler:
This is the best feature in glue, it can appeal according to the schedule. You can initiate multiple jobs in a parallel way. By using Scheduler, you can design ETL pipelines for selecting the Dependencies on many Jobs.
b)Developer End Points:
This feature is used for communicative ETL code when glue automatically produces a code. You have to debug and test it. Developer endpoints offer this service. When we use this mode, the transformations, writers, and custom readers were designed.
c)Generating the code:
With an exceptional Feature, automatically produces the code, for extracting, transforming, and loading your Data. Here the Input glue you need is the path/location that is where the data is referred to. From here, the glue designs ETL scripts, by itself to change, and enrich them.
d)Auto schema Discovery:
It allows you to set up, the crawlers that connect to many data sources. It variates the data, that obtain schema referred to data and automatically, stores it in the data catalog. ETL Jobs can implement this data for managing ETL operations.
e)Integrated Data Catalog:
It is the best metadata, that stores all data assets, in your AWS account. Your AWS account has a single Glue catalog. This is a place many systems, can process metadata.
f)Pricing of AWS Glue:
AWS Glue charges on an hourly basis. The pricing depends on Crawlers that identify the data and ETL Jobs. This will process and upload your data, and charges monthly.
g)Use Cases:
We can use Glue with many tools and applications.
3.Snowflake with AWS Glue:
It has many plugins that continuously spring with AWS Glue. Snowflake Data warehouse users can handle their program Data integration process, without worrying about physical maintenance or handling some other spark clusters and servers.
4.Aws Glue with AWS Data Lake:
It can integrate with AWS Data Lake, so the ETL process can operate to ingest it, clean, change and design data, which is more important.
5.AWS Glue for Non-native JDBC:
By default, it has old connectors for data stores that connect with JDBC. This applied in AWS or some other on the cloud, as the time they reach by an IP.
6.AWS Glue with Athena:
Here you can use the AWS glue catalog for designing databases and tables, that checked later. You can implement Athena in AWS glue for making schema and scheme-related Services in glue.
7.Challenges and limitations of AWS Glue:
1. When we compare glue with other tools, the glue has some pre-made components. It is updated by the AWS console. It is not open to all match kinds.
2. Glue operates well with ETL from JDBC and S3 data sources. In case, if you see the data from other cloud apps, file storage base and Glue are not supported.
3. With Glue data is staged on S3.
4. Glue is handled AWS service for apache spark and it is not a complete ETL solution.
5. Glue Doesn’t have support for traditional database-type queries. Only SQL Type of Queries guided with some complex virtual tables.
6. Since glue offers support for writing transformations in python and Scala, it doesn’t offer an environment for testing the transformation.
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Challenges with ETL process
The process ETL has been with us for many years and simplifies the data processing. Earlier ETL process has been considered as a batch process only that usually deals with homogenous data. But with the explosion of Big Data or a huge amount of data things gradually changed rather than an easier one in the early days. Today data comes with high volume and velocity. Moreover, it has different forms also. Thus, there is a need for change in the design of ETL with the complexity of data. Also, this is a new challenge for the ETL process developers that are engaged.
Hence, there is also a need for critical architecture, servers, and resources with the growing ETL complexity. Many large firms and enterprises can invest in building complex ETL servers with powerful resources. But there are some small companies that cannot afford much in this regard.
In the coming days, ETL will become an overhead rather than a reward. The major aim of this process is to make the data analytics-ready, extract reports, and getting meaningful insights. All this needs to bring towards the data analyst or data scientists to perform analysis of data. Further, this requires much time to spend where companies today have ended up spending money and time for this.
Thus, with the introduction of AWS Glue, the ETL process becomes smoother on the cloud. This fully managed serverless ETL service is available on the cloud platform of AWS.
AWS Glue architecture
The AWS glue architecture includes the following parts.:-
AWS Glue Data Catalog
ETL Engine
Schedulers
AWS Glue Data Catalog
The data catalog in AWS Glue is a central metadata repository where it stores the metadata. Besides, this data is of the browsing data from stores. This data browsing is done through the use of a crawler that automatically browses available data stores. While performing the actual ETL process this metadata is much useful and also the data catalog holds the metadata of various ETL jobs.
There may be a number of data stores available such as Amazon RDS, S3, Amazon Redshift, EC2, etc. After crawling completes, it develops a catalog from different resources. Later, it presents a unique view of the catalog which is queried through Amazon Athena or Redshift. Moreover, this data catalog supports all data types/formats like CSV, JSON, etc.
ETL Engine
The ETL engine works for the ETL process within AWS Glue. Here, the AWS Glue uses the catalog info and produces the ETL scripts either in Python or Scala language. Hence, the ETL scripts are mainly accountable for loading data from the source to the target or data warehouse. Also, there is flexibility to customize the data as per the need of the users.
Schedulers
For the repetitive ETL jobs, AWS Glue offers the Schedulers to perform the ETL process at scheduled frequencies. Moreover, users have the flexibility to chain multiple ETL jobs in a series for execution at a defined time.
Hence, we have gone through the various parts of the AWS Glue architecture. There is a separate value for each part and its performance in the ETL process. Now we will look at the major benefits that this platform provides.
Advantages of AWS Glue
There are many distinct advantages of using this AWS service which is as follows.:-
AWS Glue provides hassle-free ETL services through a cloud platform. Hence, there is no need to build and invest in on-premise ETL infra.
Since it is completely managed and serverless architecture it can take care of its background process. Therefore, there is no need to deploy any server for the ETL process.
The Cloud ETL service of this platform is the biggest advantage for small and even large-scale companies. Thus, it’s a cost-effective ETL process. Also, you can pay for the resources that you use in this regard while running the jobs.
Being the automatic ETL processing, there is no need to spend much time on it. The user can directly focus on the data for analytics purposes.
Moreover, AWS Glue is much effectively used in giant companies like Deloitte, Brut, Woot, Siemens, etc.
When to use AWS Glue?
After learning about AWS Glue and its advantages and features, if you are still confused about when to use it. Then look into the following content and know how it will make your process easier.
This platform is useful while developing a data warehouse to arrange, cleanse, validate, and alter the existing data. A user can change or move the existing AWS Cloud data to his data storage.
It is useful while running serverless queries against the Amazon S3 data lake or warehouse. Here the crawlers help us to store the metadata for further use and analysis. Moreover, through this Glue, a user can access this data for analysis without loading many data silos.
This AWS service is useful when a user wants to create event-driven ETL pipelines. A user can run ETL jobs as the new data gets available for use in Amazon S3.
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Furthermore, it is also useful to understand the existing data assets of the user. There are many AWS services available for storing data and maintaining a unified view of the data through the AWS Glue data catalog. It helps to view data quickly to find the datasets we have and manage the metadata in a central repository.
Hence, these are the basic conditions for using the AWS Glue service for the ETL process. This service is available with a cloud platform which makes the process much better in a very short span. Also, very cost-effective for many small & large-scale users and enterprises.
Conclusion
Hence, we learned about various aspects of AWS Glue including its uses, benefits, features, and architecture. I hope this information will give you a better understanding of the AWS Glue ETL process and its different uses. To learn more about this platform get into the AWS Online Course with OnlineITGuru. This learning can help you with real-time scenarios of AWS with expert guidance in detail.
These are the best-known facts about AWS Glue, in upcoming Blogs, we will update more Data on it.