My organization plans to store a set of data in BigQuery and would like to periodically extract some of that data and bring it back to an on-premise database. In reviewing what I've found online about Dataflow, the most common examples involve moving data in the other direction - from an on-premise database into the cloud. Is it possible to use Dataflow to bring data back out of the cloud to our systems? If not, are there other tools that are better suited to this task?
Abstractly, yes. If you've got a set of sources and syncs and you want to move data between them with some set of transformations, then Beam/Dataflow should be perfectly suitable for the task. It sounds like you're discussing a batch-based periodic workflow rather than a continuous streaming workflow.
In terms of implementation effort, there's more questions to consider. Does an appropriate Beam connector exist for your intended on-premise database? You can see the built-in connectors here: https://beam.apache.org/documentation/io/built-in/ (note the per-language SDK toggle at top of page)
Do you need custom transformations? Are you combining data from systems other than just BigQuery? Either implies to me that you're on the right track with Beam.
On the other hand, if your extract process is relatively straightforward (e.g. just run a query once a week and extract it), you may find there are simpler solutions, particularly if you're not moving much data and your database can ingest data in one of the BigQuery export formats.
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If we have an on-prem sources like SQL-Server and Oracle. Data from it has to be ingested periodically in batch mode in Big Query. What shud be the architecture? Which GCP native services can be used for this? Can Dataflow or DataProc be used?
PS: Our organization haven't licensed any third-party ETL tool so far. Preference is for google native service. Data Fusion is very expensive.
There are two approaches you can take with Apache Beam.
Periodically run a Beam/Dataflow batch job on your database. You could use Beam's JdbcIO connector to read data. After that you can transform your data using Beam transforms (PTransforms) and write to the destination using a Beam sink. In this approach, you are responsible for handling duplicate data (for example, by providing different SQL queries across executions).
Use a Beam/Dataflow pipeline that can read change streams from a database. The simplest approach here might be using one of the available Dataflow templates. For example, see here. You can also develop your own pipeline using Beam's DebeziumIO connector.
I have heterogeneous sources like flat files residing on prem, json on share point, api which serves data so and so. Which is the best etl tool to bring data to bigquery environment ?
Im a kinder garden student in GCP :)
Thanks in advance
There are many solutions to achieve this. It depends on several factors some of which are:
frequency of data ingestion
whether or not the data needs to be
manipulated before being written into bigquery (your files may not
be formatted correctly)
is this going to be done manually or is this going to be automated
size of the data being written
If you are just looking for an ETL tool you can find many. If you plan to scale this to many pipelines you might want to look at a more advanced tool like Airflow but if you just have a few one-off processes you could set up a Cloud Function within GCP to accomplish this. You can schedule it (via cron), invoke it through HTTP endpoint, or pub/sub. You can see an example of how this is done here
After several tries and datalake/datawarehouse design and architecture, I can recommend you only 1 thing: ingest your data as soon as possible in BigQuery; no matter the format/transformation.
Then, in BigQuery, perform query to format, clean, aggregate, value your data. It's not ETL, it's ELT: you start by loading your data and then you transform them.
It's quicker, cheaper, simpler, and only based on SQL.
It works only if you use ONLY BigQuery as destination.
If you are starting from scratch and have no legacy tools to carry with you, the following GCP managed products target your use case:
Cloud Data Fusion, "a fully managed, code-free data integration service that helps users efficiently build and manage ETL/ELT data pipelines"
Cloud Composer, "a fully managed data workflow orchestration service that empowers you to author, schedule, and monitor pipelines"
Dataflow, "a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing"
(Without considering a myriad of data integration tools and fully customized solutions using Cloud Run, Scheduler, Workflows, VMs, etc.)
Choosing one depends on your technical skills, real-time processing needs, and budget. As mentioned by Guillaume Blaquiere, if BigQuery is your only destination, you should try to leverage BigQuery's processing power on your data transformation.
we have a dataset of ~10 million entities or a certain Kind in Datastore. We want to change the products functionality, so we would like to change the fields on all Kind entities.
Is there a smart/quick way to do it, that does not involve iterating over all of the entities in series?
Probably you can use Dataflow to help you with your problem.
Dataflow is a stream and batch data processing service, fully managed by GCP.
It was open sourced in the Apache Beam project. It is fully compatible with this SDK. This allows you to test your developments locally before run them on GCP.
It exposes two main concepts, a PCollection, basically the data that is being handled by the tool, and pipelines, the different steps necessary to capture the data, the transformations that must be performed, and how and where the results obtained should be written.
It provides support for Java, Python and Go, and a rich feature set and variety of possible data sources and transformations.
In the specific case of Datastore, Dataflow provides support for read, write and delete data. See for instance the relevant documentation for Python.
You can see a good example of how to interact with datastore in the Apache Beam Github repository.
These two other articles could be also interesting: 1 2.
I would presume that you have to loop through each one and update it as it's a NoSQL data store like mongo from what I can see. We have a system that uses SQL and Mongo and the demoralised data is a pain, we had to write migrations that would loop through all and update.
I am new to BQ. I have a table with around 200 columns, when i wanted to get DDL of this table there is no ready-made option available. CATS is not always desirable.. some times we dont have a refernce table to create with CATS, some times we just wanted a simple DDL statement to recreate a table.
I wanted to edit a schema of bigquery with changes to mode.. previous mode is nullable now its required.. (already loaded columns has this column loaded with non-null values till now)
Looking at all these scenarios and the lengthy solution provided from Google documentation, and also no direct solution interms of SQL statements rather some API calls/UI/Scripts etc. I feel not impressed with Bigquery with many limitations. And the Web UI from Google Bigquery is so small that you need to scroll lot many times to see the query as a whole. and many other Web UI issues as you know.
Just wanted to know how you are all handling/coping up with BQ.
I would like to elaborate a little bit more to #Pentium10 and #guillaume blaquiere comments.
BigQuery is a serverless, highly scalable data warehouse that comes with a built-in query engine, which is capable of running SQL queries on terabytes of data in a matter of seconds, and petabytes in only minutes. You get this performance without having to manage any infrastructure.
BigQuery is based on Google's column based data processing technology called dremel and is able to run queries against up to 20 different data sources and 200GB of data concurrently. Prediction API allows users to create and train a model hosted within Google’s system. The API recognizes historical patterns to make predictions about patterns in new data.
BigQuery is unlike anything that has been used as a big data tool. Nothing seems to compare to the speed and the amount of data that can be fitted into BigQuery. Data views are possible and recommended with basic data visualization tools.
This product typically comes at the end of the Big Data pipeline. It is not a replacement for existing technologies but it complements them. Real-time streams representing sensor data, web server logs or social media graphs can be ingested into BigQuery to be queried in real time. After running the ETL jobs on traditional RDBMS, the resultant data set can be stored in BigQuery. Data can be ingested from the data sets stored in Google Cloud Storage, through direct file import or through streaming data
I recommend you to have a look for Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale book about BigQuery that includes walkthrough on how to use the service and a deep dive of how it works.
More than that, I found really interesting article for Data Engineers new to BigQuery, where you can find consideration regarding DDL and UI and best practices on Medium.
I hope you find the above pieces of information useful.
All the examples i've seen are with Java programs?
I want to be able to track the a user's behaviour while navigating my website by looking at all the API calls made by that user. All the API calls are based on data stored in a SQL database.
I also for example want to check all the keywords passed to my search API to have a list of most search terms.
I thought about using Oozie but does anyone have any other suggestions ?
There are several option for analyzing the data in your database.
Normal SQL experimentation
I'd suggest starting with normal SQL statements against your database to experiment with finding what data is of interest. This might be a little slow if you have millions of records, but gives you full flexibility to play around with the data.
Amazon EMR
Once you have identified the types of analysis you'd like to run on a regular basis (eg daily or weekly), you could launch an EMR cluster to perform analysis. Please note that this is a powerful but rather complex toolset and the time required to fully utilize it might not be worthwhile.
You can launch a transient cluster, which means that the cluster terminates once it has finished the jobs it has been given. Thus, the cluster can be triggered via a scheduled API call and will automatically terminate.
Amazon Athena
Amazon Athena provides an SQL interface to data stored in Amazon S3. The common use-case is to analyze log files that are in S3 without having to load them into a database. Athena is powerful and processes data in parallel to give results back very quickly.
Bottom line: Start simple. Play with the existing data to figure out what you'd like to discover. Then optimize.