I'm looking to put RDF data from DBPedia Turtle (.ttl) files into Postgres. I don't really care how the data is modelled in Postgres as long as it is a complete mapping (it would also be nice if there were sensible indexes), I just want to get the data in Postgres and then I can transform it with SQL from there.
I tried using this StackOverflow solution that leverages Python and sqlalchemy, but it seems to be much too slow (would take days if not more at the pace I observed on my machine).
I expected there might have been some kind of ODBC/JDBC-level tool for this type of connection. I did the same thing with Neo4j in less than an hour using a plugin Neo4j provides.
Thanks to anyone that can provide help.
Related
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
I have a database currently in Mongo running on an EC2 instance and would like to migrate the data to DynamoDB. Is this possible and what is the most cost effective way to achieve this?
When you ask for a "cost effective way" to migrate data, I assume you are looking for existing technologies that can ease your life. If so, you could do the following:
Export your MongoDB data to a text file, say in tsv format, using mongoexport.
Upload that file somewhere in S3.
Import this data, in S3, to DynamoDB using AWS Data Pipeline.
Of course, you should design & finalize your DynamoDB table schema before doing all this.
Whenever you are changing databases, you have to be very careful about the way you migrate data. Certain data formats maintain type consistency, while others do not.
Then there are just data formats that cannot handle your schema. For example, CSV is great at handling data when it is one row per entry, but how do you render an embedded array in CSV? It really isn't possible, JSON is good at this, but JSON has its own problems.
The easiest example of this is JSON and DateTime. JSON does not have a specification for storing DateTime values, they can end up as ISO8601 dates, or perhaps UNIX Epoch Timestamps, or really anything a developer can dream up. What about Longs, Doubles, Ints? JSON doesn't discriminate, it makes them all strings, which can cause loss of precision if not deserialized correctly.
This makes it very important that you choose the appropriate translation medium. The generally means you have to roll your own solution. This means loading up the drivers for both databases, reading an entry from one, translating, and writing to this other. This is the best way to be absolutely sure errors are handled properly for your environment, that types are kept consistently, and that the code properly translates schema from source to destination (if necessary).
What does this all mean for you? It means a lot of leg work for you. It is possible somebody has already rolled something that is broad enough for your case, but I have found in the past that it is best for you to do it yourself.
I know this post is old, Amazon made it possible with AWS DMS, check this document :
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.MongoDB.html
Some relevant parts:
Using an Amazon DynamoDB Database as a Target for AWS Database
Migration Service
You can use AWS DMS to migrate data to an Amazon DynamoDB table.
Amazon DynamoDB is a fully managed NoSQL database service that
provides fast and predictable performance with seamless scalability.
AWS DMS supports using a relational database or MongoDB as a source.
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.
Let me start by saying, what I know about Pentaho wouldn't fill up a single paragraph. I'm more knowledgeable about PostgreSQL. I'm working with some contractors that are building a set of monthly reports in Pentaho (v. 4.5) for my company. Some of the data needs to go through a ETL process and get rolled up for reporting purposes. From a dba(ish) point of view, I would like to move these tables into a separate PostgreSQL schema.
I know that Pentaho is often times used with MySQL (which doesn't have schemas) and I'm concerned this might cause problems. I've done some "googlin'" and I don't turn up a lot of hits on the topic, but I did find a closed bug from a few years ago - thus implying that the functionality should be supported.
before I do this, I would like to see if anyone knows of a reason this will fail or be a bad idea. (or if you've done it an it works great, please let me know that, too).
Final notes: I'm using PostgreSQL 9.1.5, and I don't have access to a Pentaho instance to even test this myself. And I'm hoping the good folks in the Stackoverflow community will share their expertise and save me from having to install one and the hours of playing/testing to get an idea of this is a bad idea.
EDIT:
I sort of knew this question was a bit vague, but I was hoping that some one would read it and share any experience they have. So, Let me spell it out more clearly and ask more explicit questions.
I have not done anything. I don't know Pentaho. I don't want to learn Pentaho (not that there is anything wrong with Pentaho... It's just not where my interests are right now). My company hired contractors (I did not hire them). They have experience with Pentaho, but with MySQL. They don't really know anything about PostgreSQL. There are some important difference between PostgreSQL and MySQL. Including the fact that PostgreSQL supports schemas (whereas MySQL uses separate database... similar in concept be behave differently in some ways). Some ORMs (and tools) don't really like this... for example, the Django framework still doesn't really fully support schemas in Postgresql (I know this because I use Python and Django often and my life is much better when I keep things in the "public" schema). Because of my experience with Django and PostgreSQL schemas, I'm a bit leery of moving this data to a new schema.
I do understand that where ever the tables are, they will need permissions to be able to access the data.
My explicit questions:
Do you use Pentaho to access a PostgreSQL database to access tables in schemas other than "public" (the default).
If so, does it just work (no problems)?
If you had problems, would you please be willing to share with me (and the Stackoverflow community) any online resources that helped you? Or would you be willing to detail what you remember here?
Do you know of anything that just won't work correctly? For example, an open bug in Pentaho related to this topic.
Again, it's not your standard kind of question. I'm hoping that someone out there has experience and is willing to share it here and save me from having to spend time setting up a new Pentaho instance and trying to learn Pentaho well enough to test it, etc.
Thanks.
Two paths you can take:
1) What previous post said ("Pentaho steps (table inputs, outputs, etc.) usually allow you to specify a database schema.")
2) In database connection, advanced tab, "The preferred schema name".
If you're working with different schemas, you can create one database connection per schema. With this approach you can leave schema field in input/output steps empty.
We use MS SQL server and I can tell you that Pentaho does struggle with the idea of a schema. Many of their apps allow you to select a schema but Pentaho, like you said, is built to use something like mySQL.
Make you pentaho database user work like it would be working in mySQL.
We made the database user default to dbo then we structured our tables like dbo.dimDimension,
dbo.factFactTable etc. Basically, only use dbo for Pentaho purposes. (Or whatever schema you want to default to.)
I use PDI and PgSQL extensively every day with a bunch of different schemas. It works fine. The only trouble you might run into is Pg's troublesome practice of forcing unquoted identifiers to lower instead of upper case. I soon realized everything was easier when I set the Advanced connection property to "Quote all in database".
Yes, you have to quote everything when you type SQL if PDI doesn't do it for you, but it works quite well. Haven't experimented with forcing all identifiers to lower case, but I expect that would work as well.
And yes, use the "Preferred schema nanme" as well, but be aware that some steps use that option and others don't. You can't, for example, expect it to add schema names to SQL you type into a Table Input step.
The only other issues you might run into are the limits of Pg's JDBC driver. It's not as good as SQL Server's or DB2's, but the only thing I've every had trouble with was sending error rows from a Table Output step to another step when the Table Output step was in batch mode.
Have fun learning PDI. It makes a great complement to your DBA skills.
Brian
Pentaho steps (table inputs, outputs, etc.) usually allow you to specify a database schema.
I did a quick test using PDI and our 8.4 Postgres instance and was able to explore, read from and write to tables in different schemas.
So, I think this is a reasonable direction. Hope this helps.