We have been looking into various options to try and stream db insert, update, and delete operations from an Aurora Postgres database into a Kinesis datastream. So far most references to this seem to involve one of the following two options using with WAL-logs with Lambda, or utilizing AWS's Database Migration Service.
While both options seem feasible I wanted to post to see if there are any other solutions we haven't yet considered. Does anyone have experience with other possible streaming patterns?
Thanks
Related
How to migrate my whole database which is currently in AWS RDS Postgres to AWS Redshift and also can you please help me out how can I keep both these DBs in sync. I want to sync even if any column is updated in RDS so it must get updated in Redshift also.
I know we can achieve it with AWS Glue, but the above scenario is mandatory in my case. Migration task is easy to do but to to the CDC migration is bit challenging. I am also aware about the bookmark key but my situation is bit different, I do not have any sequential column in the tables, but it has updated_at field in all the tables so this column is the only field on which I can check whether the record is processed or not so that duplicate processing may not occur and if any new data is inserted it should also get replicated in RedShift.
So, would anyone help me out to do this even by using pyspark script?
Thanks.
I would like to automatically stream data from an external PostgreSQL database into a Google Cloud Platform BigQuery database in my GCP account. So far, I have seen that one can query external databases (MySQL or PostgreSQL) with the EXTERNAL_QUERY() function, e.g.:
https://cloud.google.com/bigquery/docs/cloud-sql-federated-queries
But for that to work, the database has to be in GCP Cloud SQL. I tried to see what options are there for streaming from the external PostgreSQL into a Cloud SQL PostgreSQL database, but I could only find information about replicating it in a one time copy, not streaming:
https://cloud.google.com/sql/docs/mysql/replication/replication-from-external
The reason why I want this streaming into BigQuery is that I am using Google Data Studio to create reports from the external PostgreSQL, which works great, but GDS can only accept SQL query parameters if it comes from a Google BigQuery database. E.g. if we have a table with 1M entries, and we want a Google Data Studio parameter to be added by the user, this will turn into a:
SELECT * from table WHERE id=#parameter;
which means that the query will be faster, and won't hit the 100K records limit in Google Data Studio.
What's the best way of creating a connection between an external PostgreSQL (read-only access) and Google BigQuery so that when querying via BigQuery, one gets the same live results as querying the external PostgreSQL?
Perhaps you missed the options stated on the google cloud user guide?
https://cloud.google.com/sql/docs/mysql/replication/replication-from-external#setup-replication
Notice in this section, it says:
"When you set up your replication settings, you can also decide whether the Cloud SQL replica should stay in-sync with the source database server after the initial import is complete. A replica that should stay in-sync is online. A replica that is only updated once, is offline."
I suspect online mode is what you are looking for.
What you are looking for will require some architecture design based on your needs and some coding. There isn't a feature to automatically sync your PostgreSQL database with BigQuery (apart from the EXTERNAL_QUERY() functionality that has some limitations - 1 connection per db - performance - total of connections - etc).
In case you are not looking for the data in real time, what you can do is with Airflow for instance, have a DAG to connect to all your DBs once per day (using KubernetesPodOperator for instance), extract the data (from past day) and loading it into BQ. A typical ETL process, but in this case more EL(T). You can run this process more often if you cannot wait one day for the previous day of data.
On the other hand, if streaming is what you are looking for, then I can think on a Dataflow Job. I guess you can connect using a JDBC connector.
In addition, depending on how you have your pipeline structure, it might be easier to implement (but harder to maintain) if at the same moment you write to your PostgreSQL DB, you also stream your data into BigQuery.
Not sure if you have tried this already, but instead of adding a parameter, if you add a dropdown filter based on a dimension, Data Studio will push that down to the underlying Postgres db in this form:
SELECT * from table WHERE id=$filter_value;
This should achieve the same results you want without going through BigQuery.
I need to migrate the tables from the BigQuery to the on-prem Postgres database.
How can I efficiently achieve that?
Some thoughts that are coming
I will use Google APIs to export the data from the tables
Store it locally
And finally, import to Postgres
But I am not sure if that can be done for a huge amount of data in TBs. Also, how can I automate this process? Can I use Jenkins for that?
Exporting the data from BigQuery, store it and importing it to PostgreSQL is a good approach. Here are other two alternatives that you can consider:
1) There's a PostgreSQL wrapper for BigQuery that allows to query directly from BigQuery. Depending on your case scenario this might be the easiest way to transfer the data; although, for TBs it might not be the best approach. This suggestion was made by #David in this SO question.
2) Using Dataflow. You can create a ETL process using Apache Beam to made the transfer. Take a look at this how-to for transferring data from BigQuery to CloudSQL. You would need to adapt it for local PostgreSQL, but the idea maintains.
Here's another SO answer that gives more context on this approach.
Anyone ever tried migrating from Snowflake to Redshift? Or things to consider if you have been using Snowflake.
Need to understand the implications as providing options paper.
Thanks
P
This is a very wide topic and consideration depends on the usage, volume of data, availability, SLA etc. For migration to Redshift I recommend to review white papers and options in AWS Database Migration Service.
This is a very helpful guide on how to migrate data from snowflake to redshift cost effectively. Basically you can orchestrate migration using Glue Workflow and automate generation of COPY commands both in SF and RS.
https://aws.amazon.com/blogs/big-data/migrate-from-snowflake-to-amazon-redshift-using-aws-glue-python-shell/
I am not an HDFS nerd but coming from traditional RDMS background, I am scratching surface with newer technologies like Hadoop and Spark. Now, I was looking at my options when it comes to SQL querying on Spark data.
What I realized that Spark inherently supports SQL querying. Then I came across this link
https://www.enterprisedb.com/news/enterprisedb-announces-new-apache-spark-connecter-speed-postgres-big-data-processing
Which I am trying to make some sense of. If I am understanding it correctly. Data is still stored in HDFS format but Postgres connector is used as a query engine? If so, in presence of an existing querying framework, what new value does this postgress connector add?
Or I am misunderstanding what it actually does?
I think you are misunderstanding.
They allude to the concept of Foreign Data Wrapper.
"... They allow PostgreSQL queries to include structured or unstructured data, from multiple sources such as Postgres and NoSQL databases, as well as HDFS, as if they were in a single database. ...
"
This sounds to me like the Oracle Big Data Appliance approach. From Postgres you can look at the world of data processing it logically as though it is all Postgres, but underwater the HDFS data is accessed using Spark query engine invoked by the Postgres Query engine, but you need not concern yourself with that is the likely premise. We are in the domain of Virtualization. You can combine Big Data and Postgres data on the fly.
There is no such thing as Spark data as it is not a database as such barring some Spark fomatted data that is not compatible with Hive.
The value will be invariably be stated that you need not learn Big Data etc. Whether that is true remains to be seen.