Hi I have prepared some data from saved table from Datastore on Jupyter notebook from Azre ML studio. Now, I want to write the prepared data back to a datastore using the same notebook.
Please help me with some examples.
Note: Here i have connected my ADLS Gen2 to datastore.
Integration work include enabling all datastore types to be consumable by data prep/dataset. This is very important as data prep/dataset is the engine that powers the data ingestion story for AzureML and being able to support all datastore types is crucial in making this a reality. Runs that involves reading and writing to datastore using data prep/dataset.
The table below presents what we currently support.
Related
I'm currently using databricks and in order to test my databricks code I'm using databricks connect in VS code. While I'm using databricks connect, since yesterday suddenly it started behaving strange, while I'm submitting a code from VS Code, the databricks-connect is taking the access of the person who has created the databricks cluster, now he doesn't have adequate access over all the resources and my test cases/ code is failing due to access issue.
Some more inputs: I have a function which does an update operation in a delta table, so this means i can't use a normal hive or temp table as they doesn't support update operation.
I have tried delta-lake in local, but that seems tobe not working and also not that convenient, as i wouldn't be able to access my ADLs location (until i specifically do the configuration change)
So, mu question is , how you guys are doing a spark specific testing? (I'm using pytest).
I found we don't have much material for a databricks code testing in the internet..any help?
Using Python-3, I am trying to compare an Excel (xlsx) sheet to an identical spark table in Databricks. I want to avoid doing the compare in Databricks. So I am looking for a way to read the spark table via the Databricks api. Is this possible? How can I go on to read a table: DB.TableName?
There is no way to read the table from the DB API as far as I am aware unless you run it as a job as LaTreb already mentioned. However, if you really wanted to, you could use either the ODBC or JDBC drivers to get the data through your databricks cluster.
Information on how to set this up can be found here.
Once you have the DSN set up you can use pyodbc to connect to databricks and run a query. At this time the ODBC driver will only allow you to run Spark-SQL commands.
All that being said, it will probably still be easier to just load the data into Databricks, unless you have some sort of security concern.
I can recomend you write pyspark code in notebook, call the notebook from previously defined job, and establish connection between your local machine and databricks workspace.
You could perfom comaprision directly on spark or convert data frames to pandas if you wish. If noteebok will end comaprision, could retrun result from particular job. I think that sending all databricks tables could be impossible because of API limitation you have spark cluster to perform complex operation, API should be use to send small messages.
Officical documentation:
https://learn.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/jobs#--runs-get-output
Retrieve the output and metadata of a run. When a notebook task
returns a value through the dbutils.notebook.exit() call, you can use
this endpoint to retrieve that value. Azure Databricks restricts this
API to return the first 5 MB of the output. For returning a larger
result, you can store job results in a cloud storage service.
Does Snowflake support JDBC data sources, and if so how? I'm using Netsuite Analytics as a datasource and would like to load that to a Snowflake warehouse. The examples I'm finding for SnowFlake are file readers, I realise I can convert my netsuite data to a file and then ingest that but I'd rather remove that additonal step.
Snowflake has both ODBC and JDBC drivers that you can use. However, if you are loading a lot of data from Netsuite Analytics, most of the Snowflake drivers will actually generate files, PUT them to S3, and execute a COPY INTO statement to get the data into Snowflake for you. While it is more seamless, it is still executing that "additional step". The reason is...that's the most efficient way to get data into Snowflake, and it's not even close.
https://docs.snowflake.com/en/user-guide/odbc.html
https://docs.snowflake.com/en/user-guide/jdbc.html
No, Snowflake doesn't offer tools for loading data from JDBC or ODBC data sources. This is because Snowflake is a database platform and the functionality you're describing is that of a data integration or ETL tool. There are plenty of third party tools available that can handle this such as Matillion or Talend. Snowflake has a list of recommended technology partners on their website.
If you don't have access to an ETL tool then, as you mentioned, you can create a process yourself to export data from Netsuite to files that are uploaded to cloud storage such AWS S3. You can then set up this storage area an "external stage" and use Snowflake's COPY statement to load the data into Snowflake.
I'm looking for, with no success, how to read a Azure Synapse table from Scala Spark. I found in https://learn.microsoft.com connectors for others Azure Databases with Spark but nothing with the new Azure Data Warehouse.
Does anyone know if it is possible?
It is now directly possible, and with trivial effort (there is even a right-click option added in the UI for this), to read data from a DEDICATED SQL pool in Azure Synapse (the new Analytics workspace, not just the DWH) for Scala (and unfortunately, ONLY Scala right now).
Within Synapse workspace (there is of course a write API as well):
val df = spark.read.sqlanalytics("<DBName>.<Schema>.<TableName>")
If outside of the integrated notebook experience, need to add imports:
import com.microsoft.spark.sqlanalytics.utils.Constants
import org.apache.spark.sql.SqlAnalyticsConnector._
It sounds like they are working on expanding to SERVERLESS SQL pool, as well as other SDKs (e.g. Python).
Read top portion of this article as reference: https://learn.microsoft.com/en-us/learn/modules/integrate-sql-apache-spark-pools-azure-synapse-analytics/5-transfer-data-between-sql-spark-pool
maybe I misunderstood your question, but normally you would use jdbc connection in Spark to use data from remote database
check this doc
https://docs.databricks.com/data/data-sources/azure/synapse-analytics.html
keep in mind, Spark would have to ingest data from Synapse tables into memory for processing and perform transformations there, so it is not going to push down operations into Synapse.
Normally, you want to run SQL query against source database and only bring results of SQL into Spark dataframe.
If I have data in a Hadoop Cluster or SQL Elastic DB, is ML bringing that data onto ML servers, or leaving it on Hadoop/sql and running its analysis there?
Currently, Azure Machine Learning will bring that data onto ML servers.
Execution for each of the modules is done on AzureML's backend servers. But you can connect to the databases through either "Reader" modules, or say Python code using ODBC to issue queries to a database and get the results as the return type, in which case the query is being down on the data servers, and the results are sent to AzureML. This is useful if you want to do a data aggregation queries in Hive or SQL to reduce the size of your dataset before bringing it into AzureML.