Aws Glue is a service provided by amazon for deploying ETL jobs. It decreases the cost, and complexity, and the time that we spend in making ETL Jobs. If any company is price sensitive and if they need 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 about resources.
3. It comes with Crawlers that design metadata for viewing the data that 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 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 path/location that is where the data is referred. 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, it stores it in the data catalogue. 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, have a single Glue catalogue. 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.
We can use Glue with many tools and applications.
3.Snowflake with AWS Glue:
It has many plugins that continuously springs 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 catalogue for designing databases and tables, that checked later. You can implement Athena in AWS glue for making schema and schema 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 at the data from other cloud apps, file storage base and Glue is 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 Don’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.
These are the best-known facts about AWS Glue, in upcoming Blogs, we will update more Data on it.