Cost Effective way to connect Google BigQuery with Power BI. What intermediate layer is required in between GCP and Power BI?
You can access BigQuery directly from DataStudio using a custom query or loading the whole table. Technically nothing is necessary between BigQuery and DataStudio.
Regarding best practices, if your dashboard reads a lot of data and its constantly used it can lead to a high cost. In this case a "layer" makes sense.
If this is your case you could pre-aggregate your data in BigQuery to avoid a big amount of data to be read many times by DataStudio. My suggestion is:
Create a process (could be a scheduled query) that periodically aggregate your data and then save it in another table
In DataStudio read your data from the aggregated table
These steps can help you reducing costs and also can make your dashboards loading faster. The negative point is that if you are working with streaming data this approach in general will not let you see the most recent registries unless you run the aggregation process very constantly.
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My team is working on developing data platform using Google Cloud Platform.
We uploaded our company's data on Google Cloud Storage and try to make data mart on Bigquery.
However, in order to save GCP usage cost, we are considering to load all data from gcs to bigquery or create external table on bigquery.
Which way is more cost efficienct?
BigQuery and the external table capacity make the border between datalake (file) and data warehouse (structured data) blurry, and your question is relevant.
When you use external table, several feature are missing, like clustering and partitioning, and your file are parsed on the fly (with type casting) -> the processing time is slower and you can't control/limit the volume of data that your process. In addition of possible errors in file that will break your query
When you use native table, the data storage is optimize for the BigQuery processing, the data already clean and parsed, the table partitioned and clustered.
The question of cost is hard multiple. Firstly, we can talk about data storage. if you have file in GCS and the same data in BigQuery, you will pay the storage twice. However, after 90 days without any update, the data goes to "archive" storage mode in BigQuery and are 2 time cheaper. In addition, you can also move your GCS file to a cold storage after their integration in BigQuery.
That's for the storage. Then the processing. First of all, the processing roughly cost 10 times more than the storage, and it's the most important things to focus on. When you perform a BigQuery request, you pay for the volume of data that your query scan. If you have partitions or clusters, with BigQuery native tables, you can limit the amount of data that you scan and therefore reduce a lot the cost. With external tables, you can't use partitioning and clustering feature and therefore you always pay for the full amount of data.
Therefore, it depends (as always) on your volume of data and the frequency of the requests.
Don't forget something additional: with external table you can have error that can break your queries. In production mode, it can be dramatic. Think smart on that.
Finally, requesting external table is slower that native table (no partitioning, therefore more data to process and parsing/casting duration). Because time is money (if you have time critical queries), and that immaterial cost can also influence your choices.
The #guillaume blaquiere answer is okay, but he forget mention something important: it is possible to do partitioned queries. You can create partitioned external tables linked to a bucket in the storage. Eg:
gs://myBucket/myTable/dt=2019-10-31/lang=en/foo
gs://myBucket/myTable/dt=2018-10-31/lang=fr/bar
Then, you can use "dt" or "lang" filters in SQL queries from BigQuery.
https://cloud.google.com/bigquery/docs/hive-partitioned-queries-gcs
In my scenario, Databricks is performing read and writing transformations in Delta tables. We have PBI connected to the Databricks cluster that needs to be running most of the time, which is expensive.
Knowing that delta tables are in a container, what would be the best way in terms of cost x performance to feed PBI from delta tables?
If your set size is under max allowed size in PowerBI (100 GB I guess) and daily refresh is enough you can just load everything to your PowerBI model.
https://blog.gbrueckl.at/2021/01/reading-delta-lake-tables-natively-in-powerbi/
If you want to save the costs maybe you don't need transactions and can save it in csv in data lake, than loading everything to PowerBI and refresh daily is really easy.
If you want to save the costs and query new incoming data all the time using DirectQuery consider using Azure SQL. It has really competitive prices starting from 5 eur/usd. Integration with databricks is also perfect write in append mode do all magic.
Another option to consider is to create an Azure Synapse workspace and use serverless SQL compute to query the delta lake files. This is a pay-per-the-TB consumed pricing model so you don’t have to have your Databricks cluster running all the time. It’s a great way to load Power BI import models.
I am looking into different Big Data solutions and have not been able to find a clear answer or documentation on what might be the best approach and frameworks/services to use to address my Big Data use-case.
My Use-case:
I have a data producer that will be sending ~1-2 billion events to a
Kinesis Data Firehose delivery stream daily.
This data needs to be stored in some data lake / data warehouse, aggregated, and then
loaded into DynamoDB for our service to consume the aggregated data
in its business logic.
The DynamoDB table needs to be updated hourly. (hourly is not a hard requirement but we would like DynamoDB to be updated as soon as possible, at the longest intervals of daily updates if required)
The event schema is similar to: customerId, deviceId, countryCode, timestamp
The aggregated schema is similar to: customerId, deviceId, countryCode (the aggregation is on the customerId's/deviceId's MAX(countryCode) for each day over the last 29 days, and then the MAX(countryCode) overall over the last 29 days.
Only the CustomerIds/deviceIds that had their countryCode change from the last aggregation (from an hour ago) should be written to DynamoDB to keep required write capacity units low.
The raw data stored in the data lake / data warehouse needs to be deleted after 30 days.
My proposed solution:
Kinesis Data Firehose delivers the data to a Redshift staging table (by default using S3 as intermediate storage and then using the COPY command to load to Redshift)
An hourly Glue job that:
Drops the 30 day old time-series table and creates a new time-series table for today in Redshift if this is the first job run of a new day
Loads data from staging table to the appropriate time-series table
Creates a view on top of the last 29 days of time-series tables
Aggregates by customerId, deviceId, date, and MAX(CountryCode)
Then aggregates by customerId, deviceId, MAX(countryCode)
Writes the aggregated results to an S3 bucket
Checks the previous hourly Glue job's run aggregated results vs. the current runs aggregated results to find the customerIds/deviceIds that had their countryCode change
Writes the customerIds/deviceIds rows that had their countryCode change to DynamoDB
My questions:
Is Redshift the best storage choice here? I was also considering using S3 as storage and directly querying data from S3 using a Glue job, though I like the idea of a fully-managed data warehouse.
Since our data has a fixed retention period of 30 days, AWS documentation: https://docs.aws.amazon.com/redshift/latest/dg/c_best-practices-time-series-tables.html suggests to use time-series tables and running DROP TABLE on older data that needs to be deleted. Are there other approaches (outside of Redshift) that would make the data lifecycle management easier? Having the staging table, creating and loading into new time-series tables, dropping older time-series tables, updating the view to include the new time-series table and not the one that was dropped could be error prone.
What would be an optimal way to find the the rows (customerId/deviceId combinations) that had their countryCode change since the last aggregation? I was thinking the Glue job could create a table from the previous runs aggregated results S3 file and another table from the current runs aggregated results S3 file, run some variation of a FULL OUTER JOIN to find the rows that have different countryCodes. Is there a better approach here that I'm not aware of?
I am a newbie when it comes to Big Data and Big Data solutions so any and all input is appreciated!
tldr: Use step functions, not Glue. Use Redshift Spectrum with data in S3. Otherwise you overall structure looks on track.
You are on the right track IMHO but there are a few things that could be better. Redshift is great for sifting through tons of data and performing analytics on it. However I'm not sure you want to COPY the data into Redshift if all you are doing is building aggregates to be loaded into DDB. Do you have other analytic workloads being done that will justify storing the data in Redshift? Are there heavy transforms being done between the staging table and the time series event tables? If not you may want to make the time series tables external - read directly from S3 using Redshift Spectrum. This could be a big win as the initial data grouping and aggregating is done in the Spectrum layer in S3. This way the raw data doesn't have to be moved.
Next I would advise not using Glue unless you have a need (transform) that cannot easily be done elsewhere. I find Glue to require some expertise to get to do what you want and it sounds like you would just be using it for a data movement orchestrator. If this impression is correct you will be better off with a step function or even a data pipeline. (I've wasted way too much time trying to get Glue to do simple things. It's a powerful tool but make sure you'll get value from the time you will spend on it.)
If you are only using Redshift to do these aggregations and you go the Spectrum route above you will want to get as small a cluster as you can get away with. Redshift can be pricy and if you don't use its power, not cost effective. In this case you can run the cluster only as needed but Redshift boot up times are not fast and the smallest clusters are not expensive. So this is a possibility but only in the right circumstances. Depending on how difficult the aggregation is that you are doing you might want to look at Athena. If you are just running a few aggregating queries per hour then this could be the most cost effective approach.
Checking against the last hour's aggregations is just a matter of comparing the new aggregates against the old which are in S3. This is easily done with Redshift Spectrum or Athena as they can makes files (or sets of files) the source for a table. Then it is just running the queries.
In my opinion Glue is an ETL tool that can do high power transforms. It can do a lot of things but is not my first (or second) choice. It is touchy, requires a lot of configuration to do more than the basics, and requires expertise that many data groups don't have. If you are a Glue expert, knock you self out; If not, I would avoid.
As for data management, yes you don't want to be deleting tons of rows from the beginning of tables in Redshift. It creates a lot of data reorganization work. So storing your data in "month" tables and using a view is the right way to go in Redshift. Dropping tables doesn't create this housekeeping. That said if you organize you data in S3 in "month" folders then unneeded removing months of data can just be deleting these folders.
As for finding changing country codes this should be easy to do in SQL. Since you are comparing aggregate data to aggregate data this shouldn't be expensive either. Again Redshift Spectrum or Athena are tools that allow you to do this on S3 data.
As for being a big data newbie, not a worry, we all started there. The biggest difference from other areas is how important it is to move the data the fewest number of times. It sounds like you understand this when you say "Is Redshift the best storage choice here?". You seem to be recognizing the importance of where the data resides wrt the compute elements which is on target. If you need the horsepower of Redshift and will be accessing the data over and over again then the Redshift is the best option - The data is moved once to a place where the analytics need to run. However, Redshift is an expensive storage solution - it's not what it is meant to do. Redshift Spectrum is very interesting in that the initial aggregations of data is done in S3 and much reduced partial results are sent to Redshift for completion. S3 is a much cheaper storage solution and if your workload can be pattern-matched to Spectrum's capabilities this can be a clear winner.
I want to be clear that you have only described on area where you need a solution and I'm assuming that you don't have other needs for a Redshift cluster operating on the same data. This would change the optimization point.
To save storage cost we are planning to migrate from Aurora/Mysql to Snowflake for one of our use case where we store Audit related information .
We Store all Audit info in Aurora to gives us milliseconds latency when we combine this Aurora into Application .
We do have huge amount of Audit info size is 12 TB and has Text column also and it is growing .
Now to save cost and keeping future growth in mind we are exploring other option when we can save money and performance also can match .
while doing research cam to know about Snowflake and we are doing POC on this but i observe the search on ID on primary key does not give us performance same as Aurora Mysql .
So wanted some expert advice how can we make Snowflake as our application Back end where i can do Insert/Update/Delete and display record directly from Snowflake Database .
2022 update
Things have changed since my reply below!
Check the Snowflake Search Optimization Service:
The search optimization service can significantly improve the performance of certain types of lookup and analytical queries that use an extensive set of predicates for filtering.
https://docs.snowflake.com/en/user-guide/search-optimization-service.html
Unistore and Hybrid Tables are coming to Snowflake:
Unistore is a new workload that delivers a modern approach to working with transactional and analytical data together in a single platform.
https://www.snowflake.com/blog/introducing-unistore/
Don't do this.
I read from the requirements in the question that you are looking for a backend that will:
Retrieve rows by id in milliseconds.
Be a backend for an app that's constantly performing updates and deletes.
Those are not the strengths of Snowflake, nor what people love it for.
Read more about the strengths of Snowflake and the workloads you would use it for at https://www.snowflake.com/cloud-data-platform/.
Also, is there anything wrong with doing transforms/joins directly within BigQuery? I'd like to minimize the number of components and steps involved for a data warehouse I'm setting up (simple transaction and inventory data for a chain of retail stores.)
Well, if you go through GCS it means you are not streaming your data, and loading from file to BQ is free, and files can be up to 5TB in size. Which is sometimes and advantage, the large file capability and being free. Also streamin is realtime, and going through GCS means it's not realtime.
If you want to directly stream data into BQ tables that has a cost. Currently the price for streaming is $0.01 per 200 MB (June 2018), so around $50 for 1TB.
On the other hand, transformation can be done with SQL if you can express the task. Otherwise you have plenty of options, people most of the time us a Dataflow to transform things. See the linked tutorial for an advanced example.
Look also into
Cloud Dataprep - Data Preparation and Data Cleansing and
Google Data Studio: Easily Build Custom Reports and Dashboards
Also an advanced example:
Performing ETL from a Relational Database into BigQuery
Loading data via Cloud Storage is the fastest (and the cheapest) way.
Loading directly can be done via app (using streaming insert which add some additional cost)
For the doing transformation - if what are you plan/need to do can be done in BigQuery - you should do it in BigQuery :) - it is the best and fastest way of doing ETL.
But you should take in account cost of running query (if you not paying Google for slots - it could be 5$ per 1TB scans)
Another good options for complex ETL is using Data Flow - but it can became expensive very quick - in exchange of more flexibility.