Why AWS claims Glue as a ETL tool? We need to code everything to pull data, no inbuilt functionality provided by Glue. Any benefits of using Glue instead of Nifi or some other ingestion tools?
Glue is a good ETL tool within AWS. Especially for big data work loads. After all it is running on spark.
Glue does have the ability to produce some basic automated transformation code -> Move data from A to B and remap column names etc.
However, it's the flexibility to write custom code that really sets it apart. Using the Glue code editor, or the Pycharm IDE, you can script any transformations you need using pyspark and/or scala.
The benefits of Glue are really gained when it is used in conjunction with other AWS services. The Glue Data Catalog is shared with Athena and even AWS EMR, so you end up with a central point for your big data ecosystem.
One limitation of Glue I have found is writing large datasets to MS SQL Server (10 million rows+). Glue uses JDBC drivers, and as of 2020, there is yet to be a Microsoft JDBC connection that avails of bulk copy. So, effectively you are writing an insert statement for each row. Therefore, performance can suffer once you get into the 10s of millions of rows currently.
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If I had to perform ETL on a huge dataset(say 1Tb) stored in S3 as csv files, Both AWS Glue ETL job and AWS EMR steps can be used. Then how is AWS Glue different from AWS EMR. And which is the better solution in this case.
Most of the differences are already listed so I'll focus more on the use case specific.
When to choose aws glue
Data size is huge but structured i.e. it is in the table structure and is of known format (CSV, parquet, orc, json).
Lineage is required, if you need the data lineage graph while developing your etl job prefer developing the etl using glue native libraries.
The developers don't need to tweak the performance parameters like setting number of executors, per executor memory and so on.
You don't want the overhead of managing large cluster and pay only for what you use.
When to use EMR
Data is huge but semi-structured or unstructured where you can't take any benefit from Glue catalog.
You believe only in the outputs and lineage is not required.
You need to define more memory per executor depending upon the type of your job and requirement.
You can manage the cluster easily or if you have so many jobs which can run concurrently on the cluster saving you money.
In case of structured data, you should use EMR when you want more Hadoop capabilities like hive, presto for further analytics.
So it depends on what your use case is. Both are great service.
Glue allows you to submit ETL scripts directly in PySpark/Python/Scala, without the need for managing an EMR cluster. All setup/tear-down of infrastructure is managed.
There are also a few other managed components like Crawlers, Glue Data Catalog, etc which make it easier to work on your data.
You could use either for your use-case, Glue would be faster however you may not have the flexibility you get with EMR.
Glue uses EMR under the hood. This is evident when you ssh into the driver of your Glue dev-endpoint.
Now since Glue is a managed spark environment or say managed EMR environment, it comes with reduced flexibility. The type of workers that you can chose is limited. The number of language libraries that you can use in your spark code is limited. Glue did not support packages like pandas, numpy until recently. Apps like presto cant be integrated with Glue although Athena is a good alternative to a separate presto installation.
The main issue however is that Glue jobs have a cold start time from anywhere between 1 minute to 15 minutes.
EMR is a good choice for exploratory data analysis but for a production environment with CI/CD, Glue seems to be the better choice.
EDIT - Glue jobs no longer have a cold start wait time
From the AWS Glue FAQ:
AWS Glue works on top of the Apache Spark environment to provide a scale-out execution environment for your data transformation jobs. AWS Glue infers, evolves, and monitors your ETL jobs to greatly simplify the process of creating and maintaining jobs.
Amazon EMR provides you with direct access to your Hadoop environment, affording you lower-level access and greater flexibility in using tools beyond Spark.
Source: https://aws.amazon.com/glue/faqs/
AWS Glue is a ETL service from AWS. AWS Glue will generate ETL code in Scala or Python to extract data from the source, transform the data to match the target schema, and load it into the target
AWS EMR is a service where you can process large amount of data , its a supporting big data platform .It Supports Hadoop,Spark,Flink,Presto, Hive etc.You can spin up EC2 with the above listed softwares and make a similar ecosystem.
In your case , you want to process 1 TB of data .Now if you want do computations on the same data , you can use EMR and if you want to run the analytics on the transformed data , use Glue .
Following is something that i compiled post working on analytics projects (though a lot of it depends on use case) - but generally speaking :
Criteria
Glue
EMR
Costs
Comparatively Costlier
Much Cheaper (Due to Spot Instance Functionality, There have been cases when there are saving of upto 50% over top-off glue costs - even more depending upon the use case)
Orchestration
Inbuilt(Glue WorkFlows & Triggers)
Through Cloud Watch Triggers & Step Functions
Infra Work Required
No Infra Setup - Select Worker Type However,Roles & Permissions are needed
Identify the Type of Node Needed & Setup Autoscaling rules etc
Cluster Resiliency & Robustness
Highly Resilient (AWS MANAGED)
If Spot Instances are used then interruption might occur with 2 min notification(Though the System Recovers Automatically - For eg - Job Times might elongate)
Skill Sets Needed
PySpark & Intermediate AWS Knowledge
DevOps to Setup EMR & Manage, Intermediate Knowledge of Orchestration via Cloud Watch & Step Function, PySpark
Applicable Use Cases
Attractive Option in event: 1. You are not worried about Costs but need highly resilient infra2. Batch Setups wherein the Job might complete in fixed time3. Short RealTime Streaming Jobs which need to run for let's say hrs during a day
1. Use Case is of Volatile Clusters - Mostly Used for Batch Processing (Day MINUS Scenarios) - Thereby making a costs effective solution for Batch Jobs2. Attractive option for 24/7 Spark Streaming Programs3. You Need a Hadoop Ecosystem & Related tools (like HDFS, HIVE, HUE, Impala etc)4. You need to run Flink Programs etc5. You need control over Infra & It's tuning parameters
Also going back to OP's use case of 1TB of data processing. If its one time processing Glue should suffice, if its a Daily Once Batch EMR & GLUE will both be good (depending on how job is tuned Glue can be an attractive option), if its a multiple time daily job - then EMR is a better option (Considering balance of performance and cost)
For majority of use-cases, Spark transformations can be done on streaming data or bounded data (say from Amazon S3) using Amazon EMR, and then data can be written to S3 again with the transformed data.
The transformations can also be achieved in Amazon Redshift using the different data from S3 being loaded to different Redshift tables, and then the data from the different Redshift tables loaded to final table. (Now with Redshift spectrum, we could also select and transform data directly from S3 as well.)
With that said, I see the transformations can be done in both EMR and Redshift, with Redshift loads and transformations done with less development time.
So, should EMR be used for use-cases mainly involving streaming/unbounded data? What other use-cases is EMR preferable (I am aware Spark provides other core, sql, ml libraries as well), but just for transformation(involving joins/reducers) to be achieved, I don't see a use-case other than streaming inside EMR, when transformation can be achieved also in Redshift.
Please provide use-cases when to use EMR transformations vs Redshift transformation.
In the first instance I prefer to use Redshift for transformations as:
Development is easier, SQL rather than Spark
Maintenance / monitoring is easier
Infrastructure costs are lower assuming you can run during "off-peak"
times.
Sometimes EMR is a better option, I would consider it in these circumstances:
When you want to have raw and transformed data both on S3, e.g. a
"data lake" strategy
Complex transformations are required. Some transformations are just
not possible using Redshift such as when
managing complex and large json columns
pivoting of data dynamically (variable number of attributes)
Third party libraries are required
data sizes are so large that a much bigger redshift cluster would be needed to process the transformations.
There are other additional options other than Redshift and EMR, thsese should also be considered.
for example
Standard python or other scripting language to :
create dynamic transformation sql, which can be run in redshift
processing from csv to parquet or similar
scheduling (e.g. airflow)
AWS Athena
can be used with s3 (e.g. parquet) input and output
uses SQL (so some advantages in development time) using Presto syntax which in some cases is more powerful than Redshift SQL
can have significant cost benefits as no permanent infrastructe costs are needed, pay on usage.
AWS Batch and AWS lambda should also be considered.
I need to ETL data into my Cloud SQL instance. This data comes from API calls. Currently, I'm running a custom Java ETL code in Kubernetes with Cronjobs that makes request to collect this data and load it on Cloud SQL. The problem comes with managing the ETL code and monitoring the ETL jobs. The current solution may not scale well when more ETL processes are incorporated. In this context, I need to use an ETL tool.
My Cloud SQL instance contains two types of tables: common transactional tables and tables that contains data that comes from the API. The second type is mostly read-only in a "operational database perspective" and a huge part of the tables are bulk updated every hour (in batch) to discard the old data and refresh the values.
Considering this context, I noticed that Cloud Dataflow is the ETL tool provided by GCP. However, it seems that this tool is more suitable for big data applications that needs to do complex transformations and ingest data in multiple formats. Also, in Dataflow, the data is parallel processed and worker nodes are escalated as needed. Since Dataflow is a distributed system, maybe the ETL process would have an overhead when allocating resources to do a simple bulk load. In addition to that, I noticed that Dataflow doesn't have a particular sink for Cloud SQL. This probably means that Dataflow isn't the correct tool for simple bulk load operations in a Cloud SQL database.
In my current needs, I only need to do simple transformations and bulk load the data. However, in the future, we might want to handle other sources of data (pngs, json, csv files) and sinks (Cloud Storage and maybe BigQuery). Also, in the future, we might want to ingest streaming data and store it on Cloud SQL. In this sense, the underlying Apache Beam model is really interesting, since it offers an unified model for batch and streaming.
Giving all this context, I can see two approaches:
1) Use an ETL tool like Talend in the Cloud to help monitoring ETL jobs and maintenance.
2) Use Cloud Dataflow, since we may need streaming capabilities and integration with all kinds of sources and sinks.
The problem with the first approach is that I may end up using Cloud Dataflow anyway when future requeriments arrives and that would be bad for my project in terms of infrastructure costs, since I would be paying for two tools.
The problem with the second approach is that Dataflow doesn't seem to be suitable for simply bulk loading operations in a Cloud SQL Database.
Is there something I am getting wrong here? Can someone enlighten me?
You can use Cloud Dataflow just for loading operations. Here is a tutorial on how to perform ETL operations with Dataflow. It uses BigQuery but you can adapt it to connect to your Cloud SQL or other JDBC sources.
More examples can be found on the official Google Cloud Platform github page for Dataflow analysis of user generated content.
You can also have a look at this GCP ETL architecture example that automates the tasks of extracting data from operational databases.
For simpler ETL operations, Dataprep is an easy tool to use and provides flow scheduling as well.
I am confused about these two services. It looks that they are offering the same service. Probably the only difference is that the Glue catalog can contain a wider range of data sources. Does it mean that AWS Glue can replace Redshift?
The Comment is right , These two services are not same AWS Glue is ETL Service while AWS Redshift is Data Warehousing service.
According to AWS Documentation :
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It allows you to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
According to AWS Documentation :
AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores
You can Refer the Documentation Provided by AWS for Details but essentially these are totally different services.
I'm trying to implement, I think, a very simple process, but I don't really know what's the best approach.
I want to read a big csv (around 30gb) file from S3, make some transformation and load it into RDS MySQL and I want this process to be replicable.
I tought that the best approach was Aws data pipeline, but I've found that this service is more designed to load data from different sources to redshift after several transformtions.
I've also seen that the process of creating a pipeline is slow and a little bit messy.
Then I've found the dataduct wrapper of Coursera, but after some research, it seems that this project has been abandoned (the last commit was one year ago).
So I don't know if I should continue trying with aws data pipeline or take another approach.
I've also read about AWS Simple Workflow and Step Functions, but I don't know if it's simpler.
Then I've seen a video of AWS glue and it looks nice, but unfortunatelly it's not yet available and I don't know when Amazon will launch it.
As you see, I'm a little bit confuse, can anyone enlight me?
Thanks in advance
If you are trying to get them into RDS so you can query them, there are other options that do not require the data to be moved from S3 to RDS to do SQL like queries.
You can use Redshift spectrum to read and query information from S3 now.
Using Amazon Redshift Spectrum, you can efficiently query and retrieve structured and semistructured data from files in Amazon S3 without having to load the data into Amazon Redshift tables
Step 1. Create an IAM Role for Amazon Redshift
Step 2: Associate the IAM Role with Your Cluster
Step 3: Create an External Schema and an External Table
Step 4: Query Your Data in Amazon S3
Or you can use Athena to query the data in S3 as well if Redshift is too much horsepower for the need job.
Amazon Athena is an interactive query service that makes it easy to analyze data directly in Amazon Simple Storage Service (Amazon S3) using standard SQL.
You could use an ETL tool to do the transformations on your csv data and then load it into your RDS database. There are a number of open source tools that do not require large licensing costs. That way you can pull the data into the tool, do your transformations and then the tool will load the data into your MySQL database. For example there is Talend, Apache Kafka, and Scriptella. Here's some information on them for comparison.
I think Scriptella would be an option for this situation. It can use SQL scripts (or other scripting languages), and has JDBC/ODBC compliant drivers. With this you could create a script that would perform your transformations and then load the data into your MySQL database. And you would be using familiar SQL (I'm assuming you already can create SQL scripts) so there isn't a big learning curve.