My team does a lot of work staging data from other sources. I've worked to automate/script this process as much as possible because some of these sources have hundreds of tables to ingest. My pain point is in sharing my scripted Oozie workflows.
I am able to generate JSON files which are imported into Hue, but the JSON files do not specify what users/groups can read or modify them. This means I would have to manually go through every workflow and share them. I may be wrong, but it appears that sharing information is stored in the backend of Hue, not the workflows themselves.
Is it possible to add a key value pair inside of the JSON import file to specify the groups to share with?
or
Am I missing an easier way to bulk share?
Related
I am using SSIS2017 and part of what I am doing involves running several (30ish) SQL scripts to be output into flat files into the same folder. My question is, to do this do I have to create 30 New File Connections or is there a way to define the folder I want all the outputs to go to, and have them saved there?
I am only really thinking of keeping a tidy Connection Manager tab. If there's a more efficient way to do it than 30something file connections that would be great?
A data flow is tightly bound to the columns and types defined within for performance reasons.
If your use case is "I need to generate an extract of sales by year for the past 30ish" then yes, you can make do with a single Flat File Connection Manager because the columns and data types will not change - you're simply segmenting the data.
However, if your use case is "I need to extract Sales, Employees, Addresses, etc" then you will need a Flat File Connection Manager (and preferably a data flow) per entity/data shape.
It's my experience that you would be nicely served by designing this as 30ish packages (SQL Source -> Flat File Destination) with an overall orchestrator package that uses Execute Package Task to run the dependent processes. Top benefits
You can have a team of developers work on the individual packages
Packages can be re-run individually in the event of failure
Better performance
Being me, I'd also look at Biml and see whether you can't just script all that out.
Addressing comments
To future proof location info, I'd define a project parameter of something like BaseFilePath (assuming the most probably change is that dev I use a path of something like C:\ssisdata\input\file1.txt, C:\ssisdata\input\file3.csv and then production would be \server\share\input\file1.txt or E:\someplace\for\data\file1.txt) which I would populate with the dev value C:\ssisdata\input and then assign the value of \\server\share\input for the production to the project via configuration.
The crucial piece would be to ensure that an Expression exists on the Flat File Connection Manager's ConnectionString property to driven, in part, by the parameter's value. Again, being a programmatically lazy person, I have a Variable named CurrentFilePath with an expression like #[Project$::BaseFilePath] + "\\file1.csv"
The FFCM then uses #[User::CurrentFilePath] to ensure I write the file to the correct location. And since I create 1 package per extract, I don't have to worry about creating a Variable per flat file connection manager as it's all the same pattern.
I'm trying to programmatically add a csv file that's generated everyday to a GraphDB repository. I have already created the CSV to RDF mapping using Ontorefine. How does one use the CSV and the mapping now to add RDF triples programmatically.
Use the open source CLI https://github.com/Ontotext-AD/ontorefine-client (that's probably what #aksanoble refers to).
Please note that the CLI is not yet available in Ontotext Refine 1.0 (which was split off from GraphDB), and will be available in September. In the meantime, you could use GraphDB 9.11.
We are working on extended ETL pipeline scenarios, including
Reuse of cleaning and transformation scripts between projects
Run all cleaning, transformation and RDF data update or download steps on a new dataset automatically
BTW, is your file stored locally or accessed through a URL? We have an idea to handle the latter case specially.
The git repo for my Django app includes several .tsv files which contain the initial entries to populate my app's database. During app setup, these items are imported into the app's SQLite database. The SQLite database is not stored in the app's git repo.
During normal app usage, I plan to add more items to the database by using the admin panel. However I also want to get these entries saved as fixtures in the app repo. I was thinking that a JSON file might be ideal for this purpose, since it is text-based and so will work with the git version control. These files would then become more fixtures for the app, which would be imported upon initial configuration.
How can I configure my app so that any time I add new entries to the Admin panel, a copy of that entry is saved in a JSON file as well?
I know that you can use the manage.py dumpdata command to dump the entire database to JSON, but I do not want the entire database, I just want JSON for new entries of specific database tables/models.
I was thinking that I could try to hack the save method on the model to try and write a JSON representation of the item to file, but I am not sure if this is ideal.
Is there a better way to do this?
Overriding save method for something that can go wrong or that can take more than it should is not recommended. You usually override save when changes are simple and important.
You can use signals but in your case it's too much work. You can instead write a function to do this for you but still not exactly after you saved the data to database. You can do it right away but it's too much process unless it's so important for your file to be updated.
I recommend using something like celery to run a function in the background separated from all of your django functions. You can call it on every data update or each hour for example and edit your backup file. You can even create a table to monitor the update process.
Which solution is the best is highly depended you and how important the data is. And keep in mind that editing a file can be a heavy process too so creating a backup like everyday might be a better idea anyway.
Has anyone had much experience with data migration into and out of NetSuite? I have to export DB2 tables into MySQL, manipulate data, and then export ina CSV file. Then take a CSV file of accounts and manipulate the data again for accounts to match up from our old system to new. Anyone tried to do this in MySQL?
A couple of options:
Invest in a data transformation tool that connects to NetSuite and DB2 or MySQL. Look at Dell Boomi, IBM Cast Iron, etc. These tools allow you to connect to both systems, define the data to be extracted, perform data transformation functions and mappings and do all the inserts/updates or whatever you need to do.
For MySQL to NetSuite, php scripts can be written to access MySQL and NetSuite. On the NetSuite side, you can either do SOAP web services, or you can write custom REST APIs within NetSuite. SOAP is probably a bit slower than REST, but with REST, you have to write the API yourself (server side JavaScript - it's not hard, but there's a learning curve).
Hope this helps.
I'm an IBM i programmer; try CPYTOIMPF to create a pretty generic CSV file. I'll go to a stream file - if you have NetServer running you can map a network drive to the IFS directory or you can use FTP to get the CSV file from the IFS to another machine in your network.
Try Adeptia's Netsuite integration tool to perform ETL. You can also try Pentaho ETL for this (As far as I know Celigo's Netsuite connector is built upon Pentaho). Also Jitterbit does have an extension for Netsuite.
We primarily have 2 options to pump data into NS:
i)SuiteTalk ---> Using which we can have SOAP based transformations.There are 2 versions of SuiteTalk synchronous and asynchronous.
Typical tools like Boomi/Mule/Jitterbit use synchronous SuiteTalk to pump data into NS.They also have decent editors to help you do mapping.
ii)RESTlets ---> which are typical REST based architures by NS can also be used but you may have to write external brokers to communicate with them.
Depending on your need you can have whatever you need.IN most of the cases you will be using SuiteTalk to bring in data to Netsuite.
Hope this helps ...
We just got done doing this. We used an iPAAS platform called Jitterbit (similar to Dell Boomi). It can connect to mySql and to NetSuite and you can do transformations in the tool. I have been really impressed with the platform overall so far
There are different approaches, I like the following to process a batch job:
To import data to Netsuite:
Export CSV from old system and place it in Netsuite's a File Cabinet folder (Use a RESTlet or Webservices for this).
Run a scheduled script to load the files in the folder and update the records.
Don't forget to handle errors. Ways to handle errors: send email, create custom record, log to file or write to record
Once the file has been processed move the file to another folder or delete it.
To export data out of Netsuite:
Gather data and export to a CSV (You can use a saved search or similar)
Place CSV in File Cabinet folder.
From external server call webservices or RESTlet to grab new CSV files in the folder.
Process file.
Handle errors.
Call webservices or RESTlet to move CSV File or Delete.
You can also use Pentaho Data Integration, its free and the learning curve is not that difficult. I took this course and I was able to play around with the tool within a couple of hours.
This question is going to be a purely organizational question about SSIS project best practice for medium sized imports.
So I have source database which is continuously being enriched with new data. Then I have a staging database in which I sometimes load the data from the source database so I can work on a copy of the source database and migrate the current system. I am actually using a SSIS Visual Studio project to import this data.
My issue is that I realised the actual design of my project is not really optimal and now I would like to move this project to SQL Server so I can schedule the import instead of running manually the Visual Studio project. That means the actual project needs to be cleaned and optimized.
So basically, for each table, the process is simple: truncate table, extract from source and load into destination. And I have about 200 tables. Extractions cannot be parallelized as the source database only accepts one connection at a time. So how would you design such a project?
I read from Microsoft documentation that they recommend to use one Data Flow per package, but managing 200 different package seems quite impossible, especially that I will have to chain for scheduling import. On the other hand a single package with 200 Data Flows seems unamangeable too...
Edit 21/11:
The first apporach I wanted to use when starting this project was to extract my table automatically by iterating on a list of table names. This could have worked out well if my source and destination tables had all the same schema object names, but the source and destination database being from different vendor (BTrieve and Oracle) they also have different naming restrictions. For example BTrieve does not reserve names and allow more than 30 characters names, which Oracle does not. So that is how I ended up manually creating 200 data flows with a semi-automatic column mapping (most were automatic).
When generating the CREATE TABLE query for the destination database, I created a reusable C# library containing the methods to generate the new schema object names, just in case the methodology could automated. If there was any custom tool to generate the package that could use an external .NET library, then this might do the trick.
Have you looked into BIDS Helper's BIML (Business Intelligence Markup Language) as a package generation tool? I've used it to create multiple packages that all follow the same basic truncate-extract-load pattern. If you need slightly more cleverness than what's built into BIML, there's BimlScript, which adds the ability to embed C# code into the processing.
From your problem description, I believe you'd be able to write one BIML file and have that generate two hundred individual packages. You could probably use it to generate one package with two hundred data flow tasks, but I've never tried pushing SSIS that hard.
You can basically create 10 child packages each having 20 data flow tasks and create a master package which triggers these child pkgs.Using parent to child configuration create a single XML file configuration file .Define the precedence constraint for executing the package in serial fashion in master pkg. In this way maintainability will be better compared to having 200 packages or single package with 200 data flow tasks.
Following link may be useful to you.
Single SSIS Package for Staging Process
Hope this helps!