I have daily MySQL DB snapshots stored on S3. This daily DB snapshot is a backup of 1000 tables in our DB, using mysqldump, size is about 300M daily (stored 1 year of snapshots, which is about 110G).
Now we want to load these snapshots daily to snowflake for reporting purpose. How do we create tables in snowflake? Shall we create 1000 tables? Will snowflake be able to handle this scenario?
All comments are welcome. Thanks!
One comment before I look at possible solutions: your statement "Our purpose is to avoid creating dimension or fact tables (typical data warehouse approach) to save cost at the beginning" is the sort of thinking that can get companies into real trouble. Once you build something and start using it, in 99% of cases you will be stuck with it - so not designing a proper, supportable, reporting solution (whether it is a Kimball model or something else) from the start is always a false economy. If you take a "quick and dirty" approach now you will regret it in a year's time.
With that out of the way, there seem to be 2 issues you need to address:
How to store your data
How to process your data (to produce you metrics and whatever else you want to do with it)
Data Storage
(Probably stating the obvious) Any tables that you create to hold metrics or which will be accessed by BI tools (including direct SQL) I would hold in Snowflake - otherwise you wont get the performance that Snowflake can deliver and there is little point using Snowflake - you might as well be using Athena directly against your S3 buckets.
For your source tables (currently in S3), in an ideal world I would also copy them into Snowflake and treat S3 as your staging area - so once the data has been copied from S3 to Snowflake you can drop the data from S3 (or archive it or do whatever you want to it).
However, if you need the S3 versions of the data for other purposes (and so can't delete it once it has been copied to Snowflake) then rather than keep duplicate copies of the data you could create External Tables in Snowflake that point to your S3 buckets and don't require you to move the data into Snowflake. Query performance against External Tables will be worse than if the tables were within Snowflake, but performance may be good enough for your purposes - especially if they are "just" being used as data sources rather than for analytical queries.
Computation
There are a number of options for the technologies you use to calculate your metrics - which one you choose is probably down to your existing skillset, cost, supportability, etc.
Snowflake functionality - Stored Procedures, External Functions (still in Preview rather than GA, I believe), etc.
External coding tools: anything that can connect to Snowflake and read/write data (e.g. Python, Spark, etc.)
ETL/ELT tool - probably overkill for your specific use case but if you are building a proper reporting platform that requires an ETL tool then obviously you could use this to create your metrics as well as move your data around
Hope this helps?
Related
I have huge data from different DB sources ( Oracle, Mongo, Cassandra ) and also eventing data available in Kafka. Using Tableau for analytics and facing performance issue with huge data. So, planning to store data in some other way and use Tableau for visualization also. Have multiple options now and need some help to finalize the approach.
Option 1:-
Read DB data and store them in Parquet file and then expose it over Spark SQL or HiveQL or Presto SQL and let Tableau connect to this SQL.
Option 2:-
Read DB data and store them in Parquet file in S3 and then use AWS Athena for analytics and let Tableau connect to Athena.
Option 3:-
Read DB data and store them in Parquet file in S3 and then move to Redshift for analytics and let Tableau connect to Redshift.
Not sure if any of the above approach will be a good solution for streaming data( Kafka ) analytics as well.
Note:- I have multiple big tables and need joins b/w them.
I understand you have huge data from different sources, and you also have access to AWS. Then, you plan to use this data for analytics and dashboarding via Tableau.
Option 1 and 2
Your Options 1 and 2 are basically the same, as AWS Athena and Hive are based on the same principle of creating tables over flat files via a metastore which stores table definition. Both Athena's Presto engine and Spark are distributed and highly efficient on huge data (TB data). The main difference is the pricing model (Athena is based on price per data processed per request and is serverless, whereas Spark may imply infrastructure cost).
Then, both options may not perform well as they are not OLAP systems designed for self service BI (they are better use for ad hoc queries over huge data regarding).
Then, you may have trouble in managing your data model using flat files and table or views over them (data storage and compression won't be optimized for each table which may impact Tableau performance).
Option 3
Option 3 is better as it is based on Redshift which is designed to support OLAP system. You can connect Tableau directly to Redshift but you'll suffer from latency and you may have trouble managing your cluster load depending on the number of users and/or requests. But it can work the way you describe it.
Then, if you have performance issues, you'll be able to create data source extracts from Redshift to Tableau later on. You can also implement an intermediate database to store pre-aggregated queries (= datamarts) and connect Tableau directly to it which will avoid performing the same query on Redshift each time a dashboard is opened in Tableau (in that case Redshift also caches queries).
Then, as you need to perform multiple joins, you'll be able to optimize data storage for such queries using Redshift by setting the right partition and sort keys.
To conclude, you can also directly access flat files from Redshift using Redshift Spectrum (via Athena/Glue metastore).
Documentations:
https://docs.aws.amazon.com/redshift/latest/dg/best-practices.html
https://aws.amazon.com/fr/athena/pricing/
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 have a SQL DB which contains PHI, hosted on AWS. I want to access this data to perform analytics, however, I must de-identify the data first to comply with HIPAA.
How should I approach this? I have thought of a few approaches:
Simply de-identify the DB with SQL commands.
From now on, every time the DB is added to, add a de-identified version of that data to another DB. Then access this DB for analytics.
From now on, every time the DB is added to, add a de-identified version of that data to another table in that DB. Then access this table with SQL commands for analytics.
Which is the best approach to use to maintain compliance with HIPAA? Or, is there a better way?
Thanks!
Budget allowing, consider doing your analytics on a different system and during the ETL, de-identify the data. Changing the source system to accommodate this requirement will increase complexity to maintain and likely affect other integrations - might end up with a monolith.
There's various ways to do this: You could do a AWS DMS (with ongoing replication) with the DB as your source and S3 as target (parquet format). From there you could use Athena for analytics as jarmod highlighted, which also supports parquet format and you can even use SQL-like queries in Athena to analyze your data. There's also Redshift, send to another Relational DB, other analytics platforms etc.
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.
I know that OLAP is used in Power Pivot, as far as I know, to speed up interacting with data.
But I know that big data databases like Google BigQuery and Amazon RedShift have appeared in the last few years. Do SQL targeted BI solutions like Looker and Chart.io use OLAPs or do they rely on the speed of the databases?
Looker relies on the speed of the database but does model the data to help with speed. Mode and Periscope are similar to this. Not sure about Chartio.
OLAP was used to organize data to help with query speeds. While used by many BI products like Power Pivot and Pentaho, several companies have built their own ways of organizing data to help with query speed. Sometimes this includes storing data in their own data structures to organize the data. Many cloud BI companies like Birst, Domo and Gooddata do this.
Looker created a modeling language called LookML to model data stored in a data store. As databases are now faster than they were when OLAP was created, Looker took the approach of connecting directly to the data store (Redshift, BigQuery, Snowflake, MySQL, etc) to query the data. The LookML model allows the user to interface with the data and then run the query to get results in a table or visualization.
That depends. I have some experience with BI solution (for example, we worked with Tableau), and it can operate is two main modes: It can execute the query against your server, or can collect the relevant data and store it on the user's machine (or on the server where the app installed). When working with large volumes, we used to make Tableau query the SQL Server itself, that's because our SQL Server machine is very strong compared to the other machines we had.
In any way, even if you store the data locally and want to "refresh" it, when it updates the data it needs to retrieve it from the database, which sometimes can also be an expensive operation (depends on how your data is built and organized).
You should also notice that you compare 2 different families of products: while Google BigQuery and Amazon's RedShift are actually database engines that used to store the data and also query it, most of the BI and reporting solutions are more concerend about querying the data and visualizing it and therefore (generally speaking) are less focused on having smart internal databases (at least from my experience).