In Snowflake, There is a concept named Snowpipe which will load the data automatically to configured tables from the different data sources.
We are trying to do the normalization while loading into the snowflake via Snowpipe.
Table A:
Id & EmployerName
Table B:
Id, Employeename & EmployerID
Value in the File
Name, EmployerName
Raj, Google
Kumar, Microsoft
We are unable to populate table A & table B in a same pipe as the pipe has only one copy statement.
Is there any concept like dependent PIPE & other ways to load the lookup table first & load the main table from the sample file?
Note:
If we have two pipes we are unable to specify the dependency.
Snowpipe should be used to load data into tables as soon as the source data is available in the cloud provider's blob storage location. You cannot set up a dependancy between Snowpipes, this would add a delay into the pipeline anyway.
Your best bet is to set up two Snowpipes to load both tables as soon as data arrives in blob storage and then use Snowflake tasks to handle the dependancies and business logic.
Just a few ideas:
Set up a Snowpipe to load into a single permanent staging area (PSA) table.
Use hash codes as the surrogate key for the two separated tables
(if you have to use surrogate keys, at all).
This way you don't have to do lookups for the surrogate key values.
Your tables will look like:
TableA - EmployerHash, EmployerName;
TableB - EmployeeHash, EmployeeName, EmployerHash;
Then create a Task with a stored procedure, that will issue a multi-table insert so that you will load into the two tables at the same time by using the same source query.
(https://docs.snowflake.net/manuals/sql-reference/sql/insert-multi-table.html#insert-multi-table)
If your real table structures & processing are more complex then you can try to use the Snowflake Streams & Tasks based on the PSA table.
For a detailed example, see here: https://community.snowflake.com/s/article/Building-a-Type-2-Slowly-Changing-Dimension-in-Snowflake-Using-Streams-and-Tasks-Part-1
HTH,
Gabor
I have an idea to do for multiple table copy:
You may create a stored procedure for copy the data from source location to target table by parameter zing the table name.
Use task in snowflake schedule your stored procedure in periodic interval.
This will populate your data in you target table in given interval. Using this option the file won't be copied immediately from your source location. Have to check the option on TASK hoe to get notified on each run.
Related
I'm SUPER new to Snowflake and Snowpark, but I do have respectable SQL and Python experience. I'm trying to use Snowpark to do my data prep and eventually use it in a data science model. However, I cannot write to the database from which I'm pulling from -- I need to create all tables in a second DB.
I've created code blocks to represent both input and output DBs in their own sessions, but I'm not sure that's helpful, since I have to be in the first session in order to even get the data.
I use code similar to the following to create a new table while in the session for the "input" DB:
my_table= session.table("<SCHEMA>.<TABLE_NAME>")
my_table.toPandas()
table_info = my_table.select(col("<col_name1>"),
col("<col_name2>"),
col("<col_name3>").alias("<new_name>"),
col("<col_name4"),
col("<col_name5")
)
table_info.write.mode('overwrite').saveAsTable('MAINTABLE')
I need to save the table MAINTABLE to a secondary database that is different from the one where the data was pulled from. How do I do this?
It is possible to provide fully qualified name:
table_info.write.mode('overwrite').saveAsTable('DATABASE_NAME.SCHEMA_NAME.MAINTABLE')
DataFrameWriter.save_as_table
Parameters:
table_name – A string or list of strings that specify the table name or fully-qualified object identifier (database name, schema name, and table name).
I am making a daily import script. It collects data from various sources and dumps it into a CSV. The CSV is used to make an external table and that external table is used populate the main table, with new data.
The trouble is that the schema of external table is designed to adapt the collected data. That is if there is a new column for the collected data, the generated external table will have that.
But the same is not true for transferring the data from external table to main table. For that I was wondering if there is some in-built function or maybe some procedure can be designed to do that.
Something like when the procedure is executed, it compares the schema of both the tables and add a column to the main table if the column is not present and then proceed with insertion of data.
Is something like this doable?
I have been searching on the internet for a solution to my problem but I can not seem to find any info. I have a large single text file ( 10 million rows), I need to create an SSIS package to load these records into different tables based on the transaction group assigned to that record. That is Tx_grp1 would go into Tx_Grp1 table, Tx_Grp2 would go into Tx_Grp2 table and so forth. There are 37 different transaction groups in the single delimited text file, records are inserted into this file as to when they actually occurred (by time). Also, each transaction group has a different number of fields
Sample data file
date|tx_grp1|field1|field2|field3
date|tx_grp2|field1|field2|field3|field4
date|tx_grp10|field1|field2
.......
Any suggestion on how to proceed would be greatly appreciated.
This task can be solved with SSIS, just with some experience. Here are the main steps and discussion:
Define a Flat file data source for your file, describing all columns. Possible problems here - different data types of fields based on tx_group value. If this is the case, I would declare all fields as strings long enough and later in the dataflow - convert its type.
Create a OLEDB Connection manager for the DB you will use to store the results.
Create a main dataflow where you will proceed the file, and add a Flat File Source.
Add a Conditional Split to the output of Flat file source, and define there as much filters and outputs as you have transaction groups.
For each transaction group data output - add Data Conversion for fields if necessary. Note - you cannot change data type of existing column, if you need to cast string to int - create a new column.
Add for each destination table an OLEDB Destination. Connect it to proper transaction group data flow, and map fields.
Basically, you are done. Test the package thoroughly on a test DB before using it on a production DB.
Do you know how to transfer only new records between two different databases (ie. Oracle and MSSQL) using SSIS? There is no problem transfering new data only between two tables in the same database and server, but is this possible to do such operation between completely different servers and databases?
Ps. I know about solution using Lookup but it is not very efficient if anybody needs to check and add a lot of records (50k and more) several times per day. I would like to operate with new data only.
You have several options:
Timestamp based solution
If you have a column which stores the insertation time in the source system, you can select only the new records created since the last load. With the same logic, you can transfer modified records too, just mark the records with the timestamp value when it change.
Sequence based solution
If there is a sequence in the source table, you can load the new records based on that sequence. Query the last value from the destination system, then load avarything which is larger than that value.
CDC based solution
If you have CDC (Change Data Capture) in your source system, you can track the changes and you can load them based on the CDC entries.
Full load
This is the most resource hungry solution: you have to copy all data from the source to the destination. If you do not have any column which marks the new records, you should use this solution.
You have several options to achieve this:
TRUNCATE the destination table and reload it from source
Use a Lookup component to determine which records are missing
Load all data from source to a temporary table and write a query which retrieves the new/changed records.
Summary
If you have at least one column, which marks the new/modified records, you can use it to implement a differential/incremental load with SSIS. If you do not have any clue, which columns/rows are changed, you have to load (or at least query) all of them.
There is no solution which enables a one-query (INSERT .. SELECT) solution using multiple servers without transferring all data. (Please note, that a multi-server query using Linked Servers are transfers the data from the source system).
What about variables? Is it possible to use the same variable between different databases and servers in SSIS?
I would like to transfer last id number from a destination table and transfer it to the source table (different server!).
I can set a variable in a database scope like this:
DECLARE #Last int
SET #Last = (SELECT TOP 1 Id FROM dbo.Table_1 ORDER BY Id DESC)
SELECT *
FROM dbo.Table_2
WHERE ID > #Last;
However it works between two tables in the same database (as a SQL command) only. I can create a variable for a entire SSIS package in Variables --> Add variable, but I don't know it is possible to use the variable in a similar way as above - to keep an information about last id in a destination table and pass it to another table on a source server as data limit.
I have a desktop application through which data is entered and it is being captured in MS Access DB. The application is being used by multiple users(at different locations). The idea is to download data entered for that particular day into an excel sheet and load it into a centralized server, which is an MSSQL server instance.
i.e. data(in the form of excel sheets) will come from multiple locations and saved into a shared folder in the server, which need to be loaded into SQL Server.
There is a ID column with IDENTITY in the MSSQL server table, which is the primary key column and there are no other columns in the table which contains unique value. Though the data is coming from multiple sources, we need to maintain single auto-updating series(IDENTITY).
Suppose, if there are 2 sources,
Source1: Has 100 records entered for the day.
Source2: Has 200 records entered for the day.
When they get loaded into Destination(SQL Server), table should have 300 records, with ID column values from 1 to 300.
Also, for the next day, when the data comes from the sources, Destination has to load data from 301 ID column.
The issue is, there may be some requests to change the data at Source, which is already loaded in central server. So how to update the data for that row in the central server as the ID column value will not be same in Source and Destination. As mentioned earlier ID is the only unique value column in the table.
Please suggest some ides to do this or I've to take up different approach to accomplish this task.
Thanks in advance!
Krishna
Okay so first I would suggest .NET and doing it through a File Stream Reader, dumping it to the disconnected layer of ADO.NET in a DataSet with multiple DataTables from the different sources. But... you mentioned SSIS so I will go that route.
Create an SSIS project in Business Intelligence Development Studio(BIDS).
If you know for a fact you are just doing a bunch of importing of Excel files I would just create many 'Data Flow Task's or many Source to Destination tasks in a single 'Data Flow Task' up to you.
a. Personally I would create tables in a database for each location of an excel file and have their columns map up. I will explain why later.
b. In a data flow task, select 'Excel Source' as the source file. Put in the appropriate location of 'new connection' by double clicking the Excel Source
c. Choose an ADO Net Destination, drag the blue line from the Excel Source to this endpoint.
d. Map your destination to be the table you map to from SQL.
e. Repeat as needed for each Excel destination
Set up the SSIS task to automate from SQL Server through SQL Management Studio. Remember you to connect to an integration instance, not a database instance.
Okay now you have a bunch of tables right instead of one big one? I did that for a reason as these should be entry points and the logic to determinate dupes and import time I would leave to another table.
I would set up another two tables for the combination of logic and for auditing later.
a. Create a table like 'Imports' or similar, have the columns be the same except add three more columns to it: 'ExcelFileLocation', 'DateImported'. Create an 'identity' column as the first column and have it seed on the default of (1,1), assign it the primary key.
b. Create a second table like 'ImportDupes' or similar, repeat the process above for the columns.
c. Create a unique constraint on the first table of either a value or set of values that make the import unique.
c. Write a 'procedure' in SQL to do inserts from the MANY tables that match up to the excel files to insert into the ONE 'Imports' location. In the many inserts do a process similar to:
Begin try
Insert into Imports (datacol1, datacol2, ExcelFileLocation, DateImported) values
Select datacol1, datacol2, (location of file), getdate()
From TableExcel1
End try
-- if logic breaks unique constraint put it into second table
Begin Catch
Insert into ImportDupes (datacol1, datacol2, ExcelFileLocation, DateImported) values
Select datacol1, datacol2, (location of file), getdate()
From TableExcel1
End Catch
-- repeat above for EACH excel table
-- clean up the individual staging tables for the next import cycle for EACH excel table
truncate TableExcel1
d. Automate the procedure to go off
You now have two tables, one for successful imports and one for duplicates.
The reason I did what I did is two fold:
You need to know more detail than just the detail a lot of times like when it came in, from what source it came from, was it a duplicate, if you do this for millions of rows can it be indexed easily?
This model is easier to take apart and automate. It may be more work to set up but if a piece breaks you can see where and easily stop the import for one location by turning off the code in a section.