Experts: I have a situation where I need to transfer incremental data( every 5 minutes ) & daily data from an application database that has about 500+ tables to S3 for a lake house implementation. The data volumes for 5 minute interval is less than 0.5 million records. In the current world, there is SQL Server CDC that copies the data to another SQL ODS and gets into 2 different Data marts that's being used for Operational reporting.
Need your expertise to answer below questions
If we choose AWS Glue to transfer data to S3, do I need to write 500+ glue jobs one for each table? Is this right way of doing ? Are there any other tools or technologies that can transfer data easily.
If we had to do both incremental ( every 5 minute ) and also batch ( hourly/daily ), can the same jobs be used? if yes, where and how to configure the time period for extraction?
If more tables or columns get added in the source database , do I need to keep writing additional jobs or can I write a template job and call with parameters?
4.Are there any other tools ( apart from Glue ) and AWS cloud watch to monitor delays, failures & long running jobs
You can use AWS DMS to migrate data to s3 target. DMS also supports CDC. Whieh means it can also sync changes post initial migration.
To transfer data for example from on-prem to cloud, you need to have a replication instance. This can be any tier based on the size of data transfer.
Then a replication task has to be created. This can be execute immediately, or scheduled run at periodic intervals.
This use case can be solved by using AWS Database Migration Service. AWS Database Migration Service (AWS DMS) is a cloud service that makes it easy to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use AWS DMS to migrate your data into the AWS Cloud or between combinations of cloud and on-premises setups.
Look at the doc for more information.
AWS Database Migration Service User Guide
Related
I need to migrate data from RDS to BQ so I can run models on Vertex AI.
The tables from RDS need to be on BQ as fast as possible, with low sync delay between the main database and the BQ replica.
I want to create a trigger that when the database on RDS is updated, it will automatically update the BQ database.
I saw the BQ Data Transfer Service tool, could it work for this case?
Can I migrate more than one table per job on a trigger time basis?
BigQuery Data Transfer Service is the tool available in GCP for redshift migration to Bigquery. For requirements such as prerequisites and permission for the migration, you may refer to this GCP documentation: https://cloud.google.com/bigquery-transfer/docs/redshift-migration#overview
For realtime update requirement from redshift to Bigquery, BigQuery Data Transfer only transfers on a scheduled, managed basis. GCP Documentation: https://cloud.google.com/bigquery-transfer/docs/introduction
For your requirement of migrating more than one table per job on a trigger time basis, BigQuery has a load quota of 15 TB, per load job, per table. You may refer to this document for Quotas and Limits: https://cloud.google.com/bigquery-transfer/docs/redshift-migration#quotas_and_limits.
Given this limitation, GCP helps you to estimate how many load jobs are required by your transfers for efficency by coming up with this formula:
Number of daily jobs = Number of transfers x Number of tables x Schedule frequency x Refresh window
You may refer to this documentation for further explanation of this formula: https://cloud.google.com/bigquery-transfer/quotas#load_jobs
We have a requirement to move data from oracle Cloud storage to Azure Cloud storage.
The requirement is basically to move data from an Oracle ADW database (hosted on Oracle cloud) to Snowflake database (hosted on Azure).
Since the data volume in tables is huge (some with 60mil+ records) we do not wish to use any ETL tool and instead want to setup a pipeline as below.
Oracle ADW database -> Store data in Oracle storage --> Move data to Azure Cloud storage -> Load into Snowflake using snowpipe or similar snowflake utilities.
How should I go about this implementation?
Also share your views on whether we can use Oracle fastconnect and Azure ExpressRoute to directly pull data from Oracle Cloud onto snowflake (or into Azure storage)
I am looking for the same thing with the simplest method from Oracle (on prem but could be cloud), into Snowflake. Looks like data must be exporeted or dropped to external tables, shifted to Azure Blob storage (like AWS S3), then pushed into Snowflake using COPY INTO - basically copying on disk external tables. This is what Snowpipe does:
"Snowpipe copies the files into a queue, from which they are loaded into the target table in a continuous, serverless fashion based on parameters defined in a specified pipe object. The following table indicates the cloud storage service support for automated Snowpipe from Snowflake accounts hosted on each cloud platform:"
It's been a while since I have worked with this. The other option is GoldenGate, which was not expensive the last time I looked into it:
https://www.snowflake.com/blog/continuous-data-replication-into-snowflake-with-oracle-goldengate/
Easy, simple, fast. Anyone have any better ideas would be appreciated.
I want to load many tables which is in aws rds mysql server by using cloud data fusion. each table storage is more than about 1gb. also I found the plugin which name is "multiple database table" to load multi table. but i got a fail. Also basically when I used database source I can check my tables' schema. However, in multiple database table, i can 't find how to check table's schema. how can i use this plugin? or is there any other way to load many tables in data fusion service?
My pipeline setting was as follows.
I'm posting this Community Wiki as OP didn't provide enough details to reproduce but the below information might help someone.
There are few ways to get your data using Cloud Data Fusion, you can use pipeline, plugin, driver and a few others depending on your needs.
On the internet you can find two very well described guides with examples.
If you would like to find some information about Cloud Data Fusion with GCP products you should read Bahadir Bulut guide - How I used Google Cloud Data Fusion to create a data warehouse - Part 1 and Part 2. Also Data Fusion allows to use 150+ preconfigured connectors and transformations like Amazons S3, SQS, etc. Azure services and many more.
Another well described (which I guess would help OP) is to configure both Amazon and GCP resources and using pipelines. This guide is Building a Simple Batch Data Pipeline from AWS RDS to Google BigQuery — Part 1: Setting UP AWS Data pipeline and second part Building a Simple Batch Data Pipeline from AWS RDS to Google BigQuery — Part 2: Setting up BigQuery Transfer Service and Scheduled Query.. In short this guide describes 2 main steps:
Extract data from MYSQL RDS and bring into S3 using AWS data pipeline service
From S3, bring the file inside Bigquery using BigqQuery transfer service.
We are using a Postgres RDS instance (db.t3.2xlarge with around 2TB data). We have a multi-tenancy application so for all organizations who sign up in our product, we are creating a separate schema which replicates our data model. Now a couple of our schemas (around 5 to 10 schemas) contain a couple of big tables (around 5 to 7 big tables where each contains 10 to 200 million rows). For UI we need to show some statics as well as graphs and to calculate that statics as well as graph data we need to perform joins on big tables and it slows down our whole database server. Sometimes we need to do this type of query in night time so that users don't face any performance issues. So ss a solution we are planning to create a data lake in S3 so that all analytical load we can shift out of RDBMS and to an OLAP solution.
As a first step we need to transfer our data from RDS to S3 and also keep syncing both data sources. Can you please suggest which tool is a better choice for us considering the below requirements:
We need to update the last 3 days data on an hourly basis. We want to keep updating recent data because over the 3 day time window, it may change. After 3 days we can consider the data “at rest” and it can rest in the data lake without any future modification.
We are using a multi tenancy system currently and we are having ~350 schemas, But it will be increasing as more organizations sign up in our product.
We are planning to do ETL so in transform we are planning to join all tables and create one denormalized table and store the data in apache parque format in S3. So that we can perform analytical queries on that table using Redshift Spectrum, EMR, or some other tool.
I just found out about AWS Data Lake recently, and also based on my research (which will hopefully, assist you in the best solution possible)..
AWS Athena can partition data, and you may want to partition your data based on tenant id (customer id).
AWS Glue has crawlers:
Crawlers can run periodically to detect the availability of new data
as well as changes to existing data, including table definition
changes.
I'm creating a (modern) data warehouse in redshift. All of our infrastructure is hosted at Amazon. So far, I have setup DMS to ingest data (including changed data) from some tables of our business database (SQL Server on EC2, not RDS) and store it directly to S3.
Now I must transform and enrich this data from the S3 before I can write it to Redshift. Our DW have some tables for facts and dimensions (star schema), so, imagine a Customer dimension, it should contain not only the customer basic info, but address info, city, state, etc. This data is spread amongst a few tables in our business database.
So here's my problem, I don't have a clear idea of how to query the S3 staging area in order to join these tables and write it to my redshift DW. I want to do it using AWS services like Glue, Kinesis, etc. i.e. full serverless.
Can Kinesis accomplish this task? Would it make things easier if I moved my staging area from S3 to Redshift since all of our data is highly relational in nature anyway? If so, the question remains, how to transform/enrich data before saving it on our DW schemas? I have searched everywhere for this particular topic but information on it is scarse.
Any help is appreciated.
There are a lot of ways to do this but one idea is to query the data using Redshift Spectrum. Spectrum is a way to query S3 (called an external database) using your Redshift cluster.
Really high-level, one way to do this would be to create a Glue Crawler job to crawl your S3 bucket, which creates the External Database that Redshift Spectrum can query.
This way, you don't need to move your data into Redshift itself. Likely, you will want to keep your "staging" area in S3 and only bring into Redshift the data that is ready to be used for reporting or analytics, which would be your Customer Dim table.
Here is the documentation to do this: https://docs.aws.amazon.com/redshift/latest/dg/c-getting-started-using-spectrum.html
To schedule the ETL SQL: I don't believe there is a scheduling tool built into Redshift but you can do that in a few ways:
1) Get an ETL tool or set up CRON jobs on a server or Glue that schedules SQL scripts to be ran. I do this with a Python script that connects to the database then runs the SQL text. This would be a little bit more of a bulk operation. You can also do this in a Lambda function and have it be scheduled on a Cloudwatch trigger which can be on a cron schedule
2) Use a Lambda function that runs the SQL script that you want that triggers for S3 PUTs into that bucket. That way the script will run right when the file drops. This would be basically a realtime operation. DMS drops files very quickly so you will have files dropping multiple times per minute so that might be more difficult to handle.
One option is to load the 'raw' data into Redshift as 'staging' tables. Then, run SQL commands to manipulate the data (JOINs, etc) into the desired format.
Finally, copy the resulting data into the 'public' tables that users query.
This is a normal Extract-Load-Transform process (slightly different to ETL) that uses the capabilities of Redshift to do the transform.