I have a bunch of JSON files coming into Azure data lake gen 2, the JSON files contains new data as well as updates.
The data needs to be merged into a SQL table so I can start to do some reporting. The way I solved the problem has been to create a Azure Data factory that looks like this
Create and copy to temp table:
First I use the copy data to take the JSON and create a table from the schema and dump the content into the table.
Create delivery table:
Creates a table with the right schema if it doesn't already exsist
Merge temp with delivery:
Here I use a merge clause to cast and merge the data from the table that was created at step 1 with the table from step 2.
Delete temp data:
Deletes the table from step 1
This data factory gets triggered each time there's a new file in the data lake.
The pipeline solves my problem but I feel like there's a lot of unnecessary overhead by creating and dropping a new table each time I process a file.
Is there a way to optimize this flow, maybe by merging the JSON directly to the "Delivery" table?
Thanks in advance
Related
I have a table in BigQuery with 100 columns. Now I want to append more rows to it via Transfer but the new CSV has only 99 columns. How should I proceed with this?
I tried creating a schema and adding that column as NULLABLE but it didn't work
I am presuming your CSV file is stored in GCS Bucket and trying to use BQ Data Transfer service to load data periodically by scheduling it.
You can not directly Load/Append data into BQ Table due to schema mismatch.
But as an alternative, create a Staging table named staging_table_csv with 99 columns and Schedule a Data transfer service to load CSV to this table on Overwrite mode.
Now write a query to Append the contents of this staging table staging_table_csv to the target BQ table.
Query might look like this:
#standardSQL
INSERT INTO `project.dataset.target_table`
SELECT
*,
<DEFAULT_VALUE> AS COL100
FROM
`project.dataset.staging_table_csv`
Now schedule this query to run after the staging table is loaded
Make sure to keep a buffer between the Staging table load and the Target Table load. You can perform trials to find a suitable buffer.
For eg: If Transfer is scheduled at 12:00, Schedule Target Table load Query t 12:05 or 12:10
Note: Creating an extra Staging table would incur storage costs but
since it is overwritten for each load, historical data cost is not
incurred
I have a copy activity that takes a bunch of JSON files and merges them into a singe JSON.
I would now like to copy the merged single JSON to Azure SQL DB. Is that possible?
Ok, it appears to be working however the output in SQL is just countryCode and CompanyId
However, I need to retrieve all the financial information in the JSON as well
Azure Data Factory Copy Activity for JSON to Table in Azure SQL DB
I repro'd the same and below are the steps.
Two json files are taken as source.
Those files are merged into single file using copy activity.
Then Merged Json data is taken as source dataset in another copy activity.
In sink, dataset for Azure SQL db is created and Auto create table option is selected.
In sink dataset, edit checkbox is selected and sink table name is given.
Once the pipeline is run, data is copied to table.
I would like to load incremental data from data lake into on premise SQL, so that i created data flow do the necessary data transformation and cleaning the data.
after that i copied all the final data sink to staging data lake to stored CSV format.
I am facing two kind of issues here.
when ever i am trigger / debug to loading my dataset(data flow full activity ), the first time data loaded in CSV, if I load second time similar pipeline, the target data lake, the CSV file loaded empty data, which means, the column header loaded but i could not see the any value inside file.
coming to copy activity, which is connected to on premise SQL server, i am trying to load the data but if we trigger this pipeline again and again, the duplicate data loaded, i want to load only incremental or if updated data comes from data lake CSV file. how do we handle this.
Kindly suggest.
When we want to incrementally load our data to a database table, we need to use the Upsert option in copy data tool.
Upsert helps you to incrementally load the source data based on a key column (or columns). If the key column is already present in target table, it will update the rest of the column values, else it will insert the new key column with other values.
Look at following demonstration to understand how upsert works. I used azure SQL database as an example.
My initial table data:
create table player(id int, gname varchar(20), team varchar(10))
My source csv data (data I want to incrementally load):
I have taken an id which already exists in target table (id=1) and another which is new (id=4).
My copy data sink configuration:
Create/select dataset for the target table. Check the Upsert option as your write behavior and select a key column based on which upsert should happen.
Table after upsert using Copy data:
Now, after the upsert using copy data, the id=1 row should be updated and id=4 row should be inserted. The following is the final output achieved which is inline with expected output.
You can use the primary key in your target table (which is also present in your source csv) as the key column in Copy data sink configuration. Any other configuration (like source filter by last modified configuration) should not effect the process.
I was trying to get data from On-prem hive Source to Azure data lake gen 2 using azure data factory.
As I need to get data for multiple tables I have created and file(ex: tnames.txt) with all my table names and stored in data lake gen 2.
In Azure Data Factory created a lookup activity and passed tnames.txt file to it.
Then added a foreach activity to that lookup actvity and in foreach activity added a copy activity.
In copy activity in source, I was giving query to extract data.
Sink is datalake gen 2.
Example code:
select * from tableName
Here table is dynamically passed from tnames.txt.
But after data is copied into data lak,e I am getting headers in copied data are like:
"tablename.columnname".
For example: Table name is Employee and few columns are ID, Name, Gender,....
My resultent file columns are like Employee.ID,Employee.Name,Employee.Gender, but my requirement is just column name.
Basically tabe name is append to column name.
How to solve this issue/Is there any other way to get data for multiple tables in single pipeline/copy activity?
Check the mapping tab of your copy activity . If the mapping is enabled, clear it and use auto-create table . It will auto-generate the schema according to the source schema. No need to explicitly create the table with defined schema. Let it be auto create table. It will generate required mapping automatically.
I have a file with data integrated from 2 different sources using Azure Mapping Data Flow and loaded into an ADLS2 datalake container/folder i.e. for example :- /staging/EDW/Current/products.parquet file.
I now need to process this file in staging using Azure Mapping Data Flow and load into it's corresponding dimension table using SCD type2 method to maintain history.
However, I want to try creating & process this dimension table as "Delta" table in Azure Data Lake using Azure Mapping Data Flow only. However, since SCD type 2 requires a source lookup to check if there are any existing records/rows and if not insert all or if changed records do updates etc etc. (let's say during first time load).
For that, I need to first create a default/blank "Delta" table in Azure data lake folder i.e. for example :- /curated/Delta/Dimension/Products/. Just like we would have done if it were in Azure SQL DW (Dedicated Pool) in which we could have first created a blank dbo.dim_products table with just the schema/structure and no rows.
I am trying to implement a DataLake-House architecture implementation by utilizing & evaluating the best features of both Delta Lake and Azure Synapse Serverless SQL pool using Azure Mapping data flow - for performance, cost savings, ease of development (low code) & understanding. However, at the same time want to avoid a Logical Datawarehouse (LDW) kind of architecture implementation at this time.
For this, tried creating a new database under built-in Azure Synapse Serverless SQL pool, defined data source, format and a blank delta table/schema structure (without any rows); but no luck.
create database delta_dwh;
create external data source deltalakestorage
with ( location = 'https://aaaaaaaa.dfs.core.windows.net/curated/Delta/' );
create external file format deltalakeformat
with (format_type = delta);
drop external table products;
create external table dbo.products
(
product_skey int,
product_id int,
product_name nvarchar(max),
product_category nvarchar(max),
product_price decimal (38,18),
valid_from date,
valid_to date,
is_active char(1)
)
with
(
location='https://aaaaaaaa.dfs.core.windows.net/curated/Delta/Dimensions/Products',
data_source = deltalakestorage,
file_format = deltalakeformat
);
However, this fails since a Delta table/file requires _delta_log/*.json folder/file to be present which maintains transaction log. That means, I have to first write few (dummy) rows as in Delta format to the said target folder and then only I can read it and perform following queries used in for SCD type 2 implementation:
select isnull(max(product_skey), 0)
FROM OPENROWSET(
BULK 'https://aaaaaaaa.dfs.core.windows.net/curated/Delta/Dimensions/Products/*.parquet',
FORMAT = 'DELTA') as rows
Any thoughts, inputs, suggestions ??
Thanks!
You may try to create initial /dummy data_flow + pipiline to create this empty delta files.
It's only simple workaround.
Create CSV with your sample table data.
Create dataflow with name =initDelta
Use this CSV as source in data flow
In projection panel set up correct data types.
Add filtering after source and setup dummy filter 1=2 etc.
Add sink with delta output.
Put your initDelta dataflow into dummy pipeline and run it.
Folder structure for delta should created.
You mentioned the your initial data is in parque file. You can use this file. Schema of table(columns and data types) will be imported from file. Filter out all rows and save result as delta.
I think it should work or I missed something in your problem
I don't think you can use Serverless SQL pool to create a delta table........yet. I think it is coming soon though.