I am planning a project that would involve hosting a very large CSV or JSON file on Github which I would want to be able to query.
The challenge is using a querying the database file, rather than downloading it for parsing.
How can I efficiently query a database file without server-side logic to parse the database for the client?
Related
I am using Oracle SQL developer, it has the following tools,
DATABASE copy, DATABASE export and Migrate.
I want to move one schema and all the data in it from one server to another.
What is the difference between these options? Does anything serve what I am looking for?
Database Copy is probably what you want.
Supply two database connections, and we'll take objects and data and copy them from one database to another.
However, if your schema is large, this will be inefficient. The Copy routine does inserts, row-by-row across the jdbc connections.
Database Export takes the objects and data and offloads them to flat files. These flat files could then be used later to put in another database.
Migrate is used to take a database from SQL Server, Sybase, Teradata, Redshift, DB2, etc. to Oracle. It has an online (jdbc row-by-row) data copy and an offline (flat files for SQL Loader) data move mode. For SQL Server/Sybase, we can also translate the T-SQL stored procedures to PL/SQL.
Your solution might also lie elsewhere - Data Pump. We have a wizard for that as well, and works great for very large schemas/databases. You'll just need access to the database OS so you can put the DMP files into a Database Directory.
I am receiving .dmp and .mdb files from a customer & need to get that data into MongoDB.
Is there any way to straight import these file types into Mongo?
The goal is to programmatically ingest these into mongo in any way I can. The only rule is that customer will not change their method of data delivery, meaning I'm stuck with the .dmp and .mdb files as a source.
Any assistance would be greatly appreciated.
Here are a few options/ideas:
Convert mdb to csv, then use mongoimport --type csv to import into MongoDB.
Use an ETL tool, e.g. Pentaho, Informatica, etc. This will give you much more flexibility for doing any necessary transformation/conversion of data.
Write a custom ETL tool, using libraries that know how to read mdb and dmp files.
You don't mention how you plan to use this data, how many tables are in the database, and how normalized the tables are. Depending on the specifics of your use case, it's very possible that loading the data from Access "as is" will not be a good choice since normalized schemas are not a good fit for MongoDB and MongoDB does not natively support joins. This is where an ETL tool can help, by extracting the source data and transforming it into an appropriate JSON structure.
MongoDB has released ODBC drivers. Go Here MongoDB ODBC Drivers connect MSAccess directly to MongoDB through ODBC. Voila!
We've got a pretty big MongoDB instance with sharded collections. It's reached a point where it's becoming too expensive to rely on MongoDB query capabilities (including aggregation framework) for insight to the data.
I've looked around for options to make the data available and easier to consume, and have settled on two promising options:
AWS Redshift
Hadoop + Hive
We want to be able to use a SQL like syntax to analyze our data, and we want close to real time access to the data (a few minutes latency is fine, we just don't want to wait for the whole MongoDB to sync overnight).
As far as I can gather, for option 2, one can use this https://github.com/mongodb/mongo-hadoop to move data over from MongoDB to a Hadoop cluster.
I've looked high and low, but I'm struggling to find a similar solution for getting MongoDB into AWS Redshift. From looking at Amazon articles, it seems like the correct way to go about it is to use AWS Kinesis to get the data into Redshift. That said, I can't find any example of someone that did something similar, and I can't find any libraries or connectors to move data from MongoDB into a Kinesis stream. At least nothing that looks promising.
Has anyone done something like this?
I ended up coding up our own migrator using NodeJS.
I got a bit irritated with answers explaining what redshift and MongoDB is, so I decided I'll take the time to share what I had to do in the end.
Timestamped data
Basically we ensure that all our MongoDB collections that we want to be migrated to tables in redshift are timestamped, and indexed according to that timestamp.
Plugins returning cursors
We then code up a plugin for each migration that we want to do from a mongo collection to a redshift table. Each plugin returns a cursor, which takes the last migrated date into account (passed to it from the migrator engine), and only returns the data that has changed since the last successful migration for that plugin.
How the cursors are used
The migrator engine then uses this cursor, and loops through each record.
It calls back to the plugin for each record, to transform the document into an array, which the migrator then uses to create a delimited line which it streams to a file on disk. We use tabs to delimit this file, as our data contained a lot of commas and pipes.
Delimited exports from S3 into a table on redshift
The migrator then uploads the delimited file onto S3, and runs the redshift copy command to load the file from S3 into a temp table, using the plugin configuration to get the name and a convention to denote it as a temporary table.
So for example, if I had a plugin configured with a table name of employees, it would create a temp table with the name of temp_employees.
Now we've got data in this temp table. And the records in this temp table get their ids from the originating MongoDB collection. This allows us to then run a delete against the target table, in our example, the employees table, where the id is present in the temp table. If any of the tables don't exist, it gets created on the fly, based on a schema provided by the plugin. And so we get to insert all the records from the temp table into the target table. This caters for both new records and updated records. We only do soft deletes on our data, so it'll be updated with an is_deleted flag in redshift.
Once this whole process is done, the migrator engine stores a timestamp for the plugin in a redshift table, in order to keep track of when the migration last run successfully for it. This value is then passed to the plugin the next time the engine decides it should migrate data, allowing the plugin to use the timestamp in the cursor it needs to provide to the engine.
So in summary, each plugin/migration provides the following to the engine:
A cursor, which optionally uses the last migrated date passed to it
from the engine, in order to ensure that only deltas are moved
across.
A transform function, which the engine uses to turn each document in the cursor into a delimited string, which gets appended to an export file
A schema file, this is a SQL file containing the schema for the table at redshift
Redshift is a data ware housing product and Mongo DB is a NoSQL DB. Clearly, they are not a replacement of each other and can co-exist and serve different purpose. Now how to save and update records at both places.
You can move all Mongo DB data to Redshift as a one time activity.
Redshift is not a good fit for real time write. For Near Real Time Sync to Redshift, you should Modify program that writes into Mongo DB.
Let that program also writes into S3 locations. S3 location to redshift movement can be done on regular interval.
Mongo DB being a document storage engine, Apache Solr, Elastic Search can be considered as possible replacements. But they do not support SQL type querying capabilities.They basically use a different filtering mechanism. For eg, for Solr, you might need to use the Dismax Filter.
On Cloud, Amazon's Cloud Search/Azure Search would be compelling options to try as well.
You can use AWS DMS to migrate data to redshift now easily , you can also realtime ongoing changes with it.
I'm designing a data warehouse system, the origin data sources are two: files (hexadecimal format, record structure known) and PostgreSQL database.
The ETL phase has to read the content of the two sources (files and DB) and combining/integrating/cleaning them. After this, loading data into the DW.
For this purpose, is better a tool (for example Talend) or ad-hoc solution (writing ad-hoc routines by using a programming language)?
I would suggest you use the Bulk Loader to get your flat file into DB. This allows you to customize the loading rules and then process/cleanse the resulting data set using regular SQL (no other custom code to write)
Is there any tool to quickly convert a DB2 table rows into collection of XML documents that we can load to Marklogic?
DB2 supports the SQL/XML publishing extensions that were introduced in SQL:2003. These functions include XMLSERIALIZE, XMLELEMENT, XMLATTRIBUTE, and XMLFOREST, and are easily added to a SQL SELECT statement to produce a simple, well-formed XML document for each row in the result set. By writing queries that retrieve the table names and column layouts from DB2's catalog views, it is possible to automate the creation of the XML-publishing SELECT statements for a large number of tables.
One way of doing this would be to use the MLSQL toolkit ( http://developer.marklogic.com/code/mlsql ). It allows accessing relational databases from within your XQuery code in MarkLogic. Not sure how the returned data actually looks like, but it should be easy to process it within XQuery, and insert your data as XML into MarkLogic.
Just make sure not to try to load a million records in one statement, but instead try to spawn batches of lets say 1000 records at a time. Spawning will also allow for handling it with multiple threads, so should be faster for that reason too..
HTH!
Do you need to stream from DB2 to MarkLogic? Or can you temporarily dump all the documents to an intermediary filesystem and then read them in? If you can dump, then simply use some DB2 tooling (like #Fred's answer above) to export the rows to a bunch of XML documenets in a filesystem and use one of many methods for reading in a directory full of XML files into MarkLogic (like Information Studio (UI or apis), RecordLoader, and so on).
If you have don't want to store them in the filesystem as an intermediary, then you could write an InformationStudio plugin for MarkLogic that will pull out each row and insert a document into MarkLogic. You'd like need some web-service or rest endpoint that the plugin could call to extract the document data from DB2.
Alternatively, I suspect you could use the DB2 tooling (described by #Fred) that will let you execute some code per row of your table. If you can do that in Java (or .Net), then pull in the MarkLogic XCC APIs which will give you the ability to write documents into MarkLogic.