According to https://cloud.google.com/bigquery/docs/creating-partitioned-tables#converting_dated_tables_into_a_partitioned_table you can specify date partitioning options for BQ tables.
Specify the partitioning configuration in the Tables::insert request
Since we do a lot of our ETL pipeline using load jobs, using create_disposition='CREATE_IF_NEEDED' I was wondering if there is a way to specify table partitioning scheme with the load job configuration.
Input appreciated.
Thanks
Right now partitioned tables have to be created before a load job. We're working on the support of creating partitioned tables within a load/query job.
Very keen on this too! The Spark Connector I am using does it via load jobs
Related
I've had a look at this SO post but it's three years old and I think GCP has changed since then.
What I'm trying to do is set up a data pipeline using DataFlow jobs to copy/transform data from one GBQ project into another GBQ project.
To create a DataFlow job, you need to choose a template and there is no template that matches my needs i.e. no BQ to BQ template.
There is an option to use a custom template (which I imagine would be a python script or something along those lines), but it seems odd that there is no BQ to BQ template. Is DataFlow not the right tool for this job? Should I just use scheduled queries?
Thanks in advance
There is a way which is not very straight forward if you really want to use Dataflow template, you can use BigQuery to cloud storage template to store data in GCS and then cloud storage to BigQuery template to bring the data to destination project. However make sure you gave proper permission that is required to access the cloud storage buckets from the destination project.
If the transformations you want are not possible using SQL or not practical to use SQL, you can use Cloud Data fusion -> Integration studio. Here you can choose both source and sink as BigQuery and there are a number of options available for transformation component. It is similar to ETL tool. Data Fusion Quickstart documentation.
Otherwise, you can simply execute or schedule a query as per your requirement in BigQuery itself and save the result of the query in another table Saving query results in destination table.
I need to understand the below:
1.) How does one BigQuery connect to another BigQuery and apply some logic and create another BigQuery. For e.g if i have a ETL tool like Data Stage and we have some data been uploaded for us to consume in form of a BigQuery. So in DataStage or using any other technology how do i design the job so that the source is one BQ and the Target is another BQ.
2.) I want to achieve like my input will be a VIEW (BigQuery) and then need to run some logic on the BigQuery View and then load into another BigQuery view.
3.) What is the technology used to connected one BigQuery to another BigQuery is it https or any other technology.
Thanks
If you have a large amount of data to process (many GB), you should do the transformation of the data directly in the Big Query database. It would be very slow to extract all the data, run it through something locally, and send it back. You don't need any outside technology to make one view depend on another view, besides access to the relevant data.
The ideal job design will be an SQL query that Big Query can process. If you are trying to link tables/views across different projects then the source BQ table must be listed in fully-specified form projectName.datasetName.tableName in the FROM clauses of the SQL query. Project names are globally unique in Google Cloud.
Permissions to access the data must be set up correctly. BQ provides fine-grained control over who can access, and it is in the BQ documentation. You can also enable public access to all BQ users if that is appropriate.
Once you have that SQL query, you can create a new view by sending your SQL to Google BigQuery either through the command line (the bq tool), the web console, or an API.
1) You can use BigQuery Connector in DataStage to read and write to bigquery.
2) Bigquery use namespaces in the format project.dataset.table to access tables across projects. This allows you to manipulate your data in GCP as it were in the same database.
To manipulate your data you can use DML or standard SQL.
To execute your queries you can use the GCP Web console or client libraries such as python or java.
3) BigQuery is a RESTful web service and use HTTPS
My requirement is to migrate data from teradata database to Google bigquery database where table structure and schema remains unchanged. Later, using the bigquery database, I want to generate reports.
Can anyone suggest how I can achieve this?
I think you should try TDCH to export data to Google Cloud Storage in Avro format. TDCH runs on top of hadoop and exports data in parallel. You can then import data from avro files into BigQuery.
I was part of a team that addressed this issue in a Whitepaper.
The white paper documents the process of migrating data from Teradata Database to Google BigQuery. It highlights several key areas to consider when planning a migration of this nature, including the rationale for Apache NiFi as the preferred data flow technology, pre-migration considerations, details of the migration phase, and post-migration best practices.
Link: How To Migrate From Teradata To Google BigQuery
I think you can also try to use cloud composer(apache airflow) or install apache airflow in instance.
If you can open the ports from Teradata DB then you can run 'gsutil' command from there and schedule it via airflow/composer to run the jobs on daily basis. Its quick and you can leverage the scheduling capabilities of airflow.
BigQuery introduced Migration Service which is a comprehensive solution for migrating the data warehouse to BigQuery. It includes free to use tools that help with each phase of migration including assessment and planning to execution and verification.
Reference:
https://cloud.google.com/bigquery/docs/migration-intro
I want to design Web UI which fetches data from HDFS. I want to generate some reports using this data which is stored in HDFS. I have my own custom reports format. I am writing REST API's to fetch data. But running HIVE queries gives latency issues Hence I want different approach for this, I could think of two.
Using IMPALA to create tables. But I am not sure about REST support for IMPALA.
Using HIVE but instead of MR use SPARK as execution engine. .
spark-job-server provides REST support, and fetch data with SPARK-SQL.
Which of the approach will be suitable or is there any better approach for this?
Please can anyone help as I am very new in this.
I'd prefer to choose impala if latency is the main consideration. It's dedicated to SQL processing on hdfs and does it well. About REST api and the application logic you are achieving, this seems to be a good example
Can one hive instance store different tables across hdfs clusters. and then do hive ql on these tables?
My use case is that I have one hive table on one hdfs cluster. I want to do some process on it with hive ql and have the output been written to another hdfs cluster. I wish to achieve this directly only by hive, not need to run through some dump / copy / import process. So Is that possible? I don't really think it is possible, however, I notice a design page on :
https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=27837073
in it , it said that :
"Note that, even today, different partitions/tables can span multiple dfs's, and hive does not enforce any restrictions. Those dfs's can be in different data centers also"
except this, I failed to google anything related.
anyone have any ideas on this? Thanks.
There are multiple ways to handle this. you can go with mirroring (use tools like Apache Falcon). In this case you have data stored in both the clusters. If you want to query across clusters having a different table without mirroring then use tools like Apache Drill which can join data from different datasources. it currently supports hive,mongo,json, kudu etc