I am working On Databricks. I have curated data in form of fact and dims. Theses data consume for power bi reporting by synapse. I am not sure what is the use of synapse, If data is already cook in databricks layer. why we are using synapse in this framework.
why we are using synapse in this framework
An analytics service for data warehouses and large data is called Azure Synapse. We can combine Azure services like Power BI, Machine Learning, and others using Azure Synapse.
It offers a number of connectors that make it easier to transfer a sizable volume of data between Azure Databricks and Azure Synapse. It also offers a mechanism for Azure Databricks users to connect to Azure Synapse.
Additionally, Azure Synapse offers SQL pools for the computing environment and data warehousing.
Related
I have below existing architecture hosted in aws env
There is a FDMEE tool configured in ec2 which load data from HFM(outside aws) and dump in to the RDS-sql db, later this data is read in power BI in azure(through a gateway)- here we face some issue while refreshing the data set
Due to some business reason, connection from HFM to FDMEE should retain in AWS, however the data flow from FDMEE to RDS are subjected to change
So we are looking for a possibility to replace RDS-sql with azure synapse analytics, so that it become more efficient to refresh data in to power bi
so how can make a stable connection from FDMEE (which is in ec2- VPC) to azure synapse analytics ?
For data on a private network you need to deploy a Self-Hosted Integration Runtime to load the data into Synapse, or push the data somewhere Synapse can access it directly, like S3 or Azure Storage.
We are going to move from SQL server to Snowflake as our target database for the warehouse.
Today we have most of our ETL development done in ODI (Oracle Data Ingegrator).
So I'm intressted in to know if anyone is using ODI together with Snowflake and how it's woking.
And what experince/recommendations you have of other ETL tools together with Snowflake as target.
For example
Matillion
DBT
Xplenty
Today we have started with using NIFI moving the data from source to Azure blob storage.
But we are not sure if ODI is the right tool for the rest when we are in the cloud.
I'm really looking forward to see all your answers
Snowflake supports both transformations during (ETL) or after loading (ELT).
Snowflake works with a wide range of data integration tools, including Informatica, Talend, Tableau, Matillion and others.
In data engineering, new tools and self-service pipelines are eliminating traditional tasks such as manual ETL coding and data cleaning companies. With easy ETL or ELT options via Snowflake, data engineers can instead spend more time working on critical data strategy and pipeline optimization projects.
With a Snowflake as your data lake and data warehouse, ETL can be effectively eliminated, as no pre-transformations or pre-schemas are needed.
In addition, Snowflake Snowpark is designed to make building complex data pipelines a breeze and to allow developers to interact with Snowflake directly without moving data. Read more about Snowpark here.
https://www.snowflake.com/trending/etl-tools
If you started to transfer data from the source to Azure blob storage, I assume that you have a subscription in Azure and it is possible that Snowflake itself is placed in the Azure region.
In this case, I recommend using Azure Data Factory directly, so you have everything on one provider and support for data migration from SQL Server.
Link to documentation: Copy and transform data in Snowflake using Azure Data Factory
I am looking for scope where i can send data from oracle db to AWS Data exchange without any manual intervention?
In January 2022, AWS Data Exchange launched support for data sets backed by Amazon Redshift; the same guide referenced by John Rotenstein, above, shows you how you can create a data set using Amazon Redshift datashares. If you are able to move data from the Oracle database to Amazon Redshift, this option may work for you.
AWS Data Exchange just announced a preview of data sets using AWS Lake Formation, which allows you to share data from your Lake Formation data lake, which has support for Oracle databases running in Amazon Relational Database Service (RDS) or hosted in Amazon Elastic Compute Cloud (EC2). Steps to create this kind of product can be found here.
I was following the tutorial about data transformation with azure databricks, and it says before loading data into azure synapse analytics, the data transformed by azure databricks would be saved on temp storage in azure blob storage first before loading into azure synapse analytics. Why the need to save it to a temp storage before loading into azure synapse analytics?
The Azure storage container acts as an intermediary to store bulk data when reading from or writing to Azure Synapse. Spark connects to the storage container using one of the built-in connectors: Azure Blob storage or Azure Data Lake Storage (ADLS) Gen2.
The following architecture diagram shows how this is achieved with each HDFS bridge of the Data Movement Service (DMS) service on every Compute node connecting to an external resource such as Azure Blob Storage. PolyBase then bidirectionally transfers data between SQL Data Warehouse and the external resource providing the fast load performance.
Using PolyBase to extract, load and transform data
The steps for implementing a PolyBase ELT for SQL Data Warehouse are:
Extract the source data into text files.
Load the data into Azure Blob storage, Hadoop, or Azure Data Lake Store.
Import the data into
SQL Data Warehouse staging tables using PolyBase.
Transform the data(optional).
Insert the data into production tables.
Good day,
I have an Oracle DB and I need to load some tables so I can query them in BigQuery.
¿Is there a way of loading the data automatically, every 24 h, to Google BigQuery?
Any way would work. It could be loading into Data Storage and creating the tables from there, or loading into Google drive from the server.
I really need some ideas, I have read a lot of articles with no luck.
Check this tutorial by Progress:
https://www.progress.com/tutorials/cloud-and-hybrid/etl-on-premises-oracle-data-to-google-bigquery-using-google-cloud-dataflow
In this tutorial the main goal will be to connect to an On-Premises Oracle database, read the data, apply a simple transformation and write it to BigQuery. The code for this project has been uploaded to GitHub for your reference.
This solution uses Dataflow and Progress' Hybrid Data Pipeline tool:
Google Cloud Dataflow is a data processing service for both batch and real-time data streams. Dataflow allows you to build pipes to ingest data, then transform and process according to your needs before making that data available to analysis tools. DataDirect Hybrid Data Pipeline can be used to ingest both on-premises and cloud data with Google Cloud Dataflow.