We are working on building a ETL pipeline. And I have come across these two plugins for postgres to capture the writes and deletes of the table.
Can someone please explain the differences between Wal2json and Pgoutput? What are the pros/cons and performance impact if there are any?
Basically I want to understand when to use among these two plugins in Postgres.
Thanks in advance
They are for different purposes. pgoutput is for use with logical replication between PostgreSQL databases, wal2json is for getting a JSON representation of data modifications for use with third-party applications.
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I'm getting familiar with the greenplum solution concepts, and trying to understand whether, and if so, when the organisation I work for should use this solution. Our conceptual idea is to setup a kind of central 'datastore' suitable for both OLTP and OLAP access.
My research: this article suggests Greenplum is more suitable for OLAP, and PostgreSQL for OLTP. But I also read about Greenplum improvements for OLTP processing. And in favour of Postgresql, there are also articles like this that suggest that OLAP (eg, a datawarehouse implementation) can be done by means of Postgresql.
So my question is: how to move forward, and what are the main criteria to decide? For example, in case we now have a just a few TB's (1-5), start with a Postgresql cluster (for OLTP+OLAP), and when data volumes grow, move on to Greenplum? Or start straight away with Greenplum?
maybe use postgres if it can handle your use case. If you have you have too much data and need to finish reports and analytics faster; change to greenplum
We are using dbt to manage our data models in MSSQL, and we are considering moving to PostgreSQL Citus.
I'm not clear on how I could use Citus's features via dbt? I'm thinking specifically of data compression and columnar storage.
Any ideas?
TBH, I don't have any experiences with dbt.
However, given that dbt docs state that they support PostgreSQL, and Citus Columnar is a table-access-method level abstraction that doesn't require any changes to the queries(*), I wouldn't expect any compatibility issues when combining both.
My guess is that after creating your tables with USING columnar option, then you can just follow dbt docs for the rest.
(*): You might still want to have a look into that page to see what Citus Columnar does or doesn't support atm.
https://github.com/citusdata/citus/tree/master/src/backend/columnar#introduction
was wondering if you had any updates on how this process has gone for you?
I'm looking to implement DBT at my company and was considering postgres/citus as well.
I believe you could use a combination of pre-hook/post-hook dbt functions and materializing incrementally instead of the table or view method.
Thinking is either pre-hook create columnar table and use the dbt incremental update, or materialize as view/table then post-hook SELECT alter_table_set_access_method('table_name', 'columnar');
We have data existing in HBase and we want to move to AWS Aurora (MySQL) and we need to use the existing data so have to somehow load the NoSQL data into Aurora.
It's not a very big data base. Just a few tables.
Are there any best practices/tools to migrate data from NoSQL to a relational DB? I saw a lot of questions on the internet that ask to the reverse (DB -> NoSQL) but my requirement is a bit different and I don't find any helpful information.
Can someone please help? Where do I even start?
One simple way to do this without writing too much custom code would be to use Spark-HBase Connector from Hortonworks (SHC) to read data from an HBase table into a Spark dataframe and to write that dataframe into a MySQL table. The key challenge would be to get SHC to work, because in my experience it's extremely version sensitive. So the trick is to correctly coordinate your version of Spark, HBase, and SHC (and finding that right combination is trickier than you may think).
However, if you manage to get all the dependencies right, then doing the above is a matter of a few lines of code in Jupyter Notebook or Pyspark. You could run this on Yarn to parallelize the workload, in case it's large. Should work. Give it a try.
I am assembling a Business Intelligence solution using the Pentaho software as a BI engine. Within this solution, I had to set up a requirements for a PostgreSQL database server.
The current situation is very easy, since no ETL process is being carried out for data extraction, so the PostgreSQL configuration has not changed it much, and it is practically as it is configured as "factory".
I would like to know what Postgres configuration parameters have to be touched and modified to optimize it as a Datawarehouse. I have seen a lot of documentation, but it is not clear to me at all, since one documentation says that such values have to be modified, and other documentation, other completely different values.
I would like to know just that, if there is a clearer and more precise documentation to optimize a postgres 9.6 to be used as a Pentaho DW.
Thank you very much
There is a web application which is running for a years and during its life time the application has gathered a lot of user data. Data is stored in relational DB (postgres). Not all of this data is needed to run application (to do the business). However form time to time business people ask me to provide reports of this data data. And this causes some problems:
sometimes these SQL queries are long running
quires are executed against production DB (not cool)
not so easy to deliver reports on weekly or monthly base
some parts of data is stored in way which is not suitable for such
querying (queries are inefficient)
My idea (note that I am a developer not the data mining specialist) how to improve this whole process of delivering reports is:
create separate DB which regularly is update with production data
optimize how data is stored
create a dashboard to present reports
Question: But is there a better way? Is there another DB which better fits for such data analysis? Or should I look into modern data mining tools?
Thanks!
Do you really do data mining (as in: classification, clustering, anomaly detection), or is "data mining" for you any reporting on the data? In the latter case, all the "modern data mining tools" will disappoint you, because they serve a different purpose.
Have you used the indexing functionality of Postgres well? Your scenario sounds as if selection and aggregation are most of the work, and SQL databases are excellent for this - if well designed.
For example, materialized views and triggers can be used to process data into a scheme more usable for your reporting.
There are a thousand ways to approach this issue but I think that the path of least resistance for you would be postgres replication. Check out this Postgres replication tutorial for a quick, proof-of-concept. (There are many hits when you Google for postgres replication and that link is just one of them.) Here is a link documenting streaming replication from the PostgreSQL site's wiki.
I am suggesting this because it meets all of your criteria and also stays withing the bounds of the technology you're familiar with. The only learning curve would be the replication part.
Replication solves your issue because it would create a second database which would effectively become your "read-only" db which would be updated via the replication process. You would keep the schema the same but your indexing could be altered and reports/dashboards customized. This is the database you would query. Your main database would be your transactional database which serves the users and the replicated database would serve the stakeholders.
This is a wide topic, so please do your diligence and research it. But it's also something that can work for you and can be quickly turned around.
If you really want try Data Mining with PostgreSQL there are some tools which can be used.
The very simple way is KNIME. It is easy to install. It has full featured Data Mining tools. You can access your data directly from database, process and save it back to database.
Hardcore way is MADLib. It installs Data Mining functions in Python and C directly in Postgres so you can mine with SQL queries.
Both projects are stable enough to try it.
For reporting, we use non-transactional (read only) database. We don't care about normalization. If I were you, I would use another database for reporting. I will desing the tables following OLAP principals, (star schema, snow flake), and use an ETL tool to dump the data periodically (may be weekly) to the read only database to start creating reports.
Reports are used for decision support, so they don't have to be in realtime, and usually don't have to be current. In other words it is acceptable to create report up to last week or last month.