I need some guidance on our strategy for loading data into a Redshift Data Warehouse for analytics. We have ~40 SQL databases, each represents one customer and each database is identical. I have a SQL database with the same table structure as the 40 but each table has an additional column called "customer" that will capture where that record came from. We do some additional ETL processing with the records as they come in.
In total we have about 50 GB of data across all 40 DBs. Looking into the recommended processes for Updating / Inserting data on AWS's site they recommend creating the scratch table then merging data. I could do this but I could also just drop all the data from a table and re-load it since I am reading from the source every time. What is the recommended way to handle this?
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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.
On current project we have a webapp with analytics module. The users select some filters and based on those filters table or graph is shown. We want the module to be responsive, so when the users select the filters it can get data in matters of seconds.
User filters are querying a large table ~1,000,000,000 rows and 20 columns (for a few years it should grow 2x/year in rows). 18 out of 20 columns are filtrable. And mostly there will be SELECT + WHERE queries.
We are not sure, should we use Data Warehouses or classical DBs.
Current reasearch suggests we should discuss between Clickhouse, DynamoDB, Snowflake, BigQuery or Redshift. Has anyone had similar use cases and which database solution would you recommend?
Since you are using the database for analytics purposes, it is recommended to use a OLAP ( Redshift)..
an OLAP database is designed to process large datasets quickly to answer questions about data.
You can compare the pricing here
https://medium.com/2359media/redshift-vs-bigquery-vs-snowflake-a-comparison-of-the-most-popular-data-warehouse-for-data-driven-cb1c10ac8555
I am currently writing a framework to transfer hive tables to aws. We can't do that in one shot. We need to it over a period of time. And so there are lots of table which needs to be in Synch with AWS and On-prem hadoop.
Tables which are small and needs truncte and load is not an issue. We have a frameowrk which daily refreshes the table using spark framework.
Problem is for huge tables, we need to append only newly added/updated/deleted rows to AWS. Finding a newly added is fairly simple task. However how do I get updated or deleted records.
40% of our total tables are transcation table. so Updates and deletes are frequent.
For other 60% tables Update/deletes are not frequent. However, sometime due to data issue, people delete the past batch and reload the data.
My questions are
Is there a way I can get Change data capture for hive table?
How do I figure out which records are updated/deleted in transcational table?
how do I figure out which records are updated/deleted in External Table?
My dataset had around 30000 tables. I have archived them all into 300 partitioned tables now. I Have deleted 29700 tables. The data volume is same as deleted tables were all archived first. Will it affect processing time of python scripts that use this dataset for creating new tables daily?
PS: I am not concerned about processes that use the archived tables. I am concerned about the processes that only uses the same dataset to create their new tables.
BigQuery doesn't mind if you have 3 tables or 30,000 tables. That shouldn't affect querying speed.
But! Imagine if a UI tries to list all tables in one dataset, or similar operations in other environments. That will be slower for sure.
I am looking to stream some data into Big Query and had a question around Step 3 of Google's best practices for Streaming data into Big Query. The process makes sense at a high level but I'm struggling with the implementation for step 3. (I am looking to use the datastore as my transactional data store.) For step 3 says to "reconciled data from the transactional data store and truncate the unreconciled data table.". My question is this; If my reconciled data is in the Google Datastore is there a way to automate the backup and deletion of this data without manually intervention?
I know I could achieve this recommended practice by using the Datastore Admin. I could:
1) Pause all writes to the datastore
2) Backup the datastore table to Cloud Storage
3) Delete all entities in the table I just backed up.
4) Import the backup into Big Query
Is there a way I can automate this so I don't have to do it manually at regular intervals?
Real-time dashboards and queries
In certain situations, streaming data into BigQuery enables real-time analysis over transactional data. Since streaming data comes with a possibility of duplicated data, ensure that you have a primary, transactional data store outside of BigQuery.
You can take a few precautions to ensure that you'll be able to perform analysis over transactional data, and also have an up-to-the-second view of your data:
1) Create two tables with an identical schema. The first table is for the reconciled data, and the second table is for the real-time, unreconciled data.
2) On the client side, maintain a transactional data store for records.
Fire-and-forget insertAll() requests for these records. The insertAll() request should specify the real-time, unreconciled table as the destination table.
3) At some interval, append the reconciled data from the transactional data store and truncate the unreconciled data table.
4) For real-time dashboards and queries, you can select data from both tables. The unreconciled data table might include duplicates or dropped records.