I would like to know what are the ways I can Query a large set of my logs files stored in my storage account. Kusto Query most of the time Times-out so cant use ADX/log-analytics on top of Storage account. What are the other ways we have? Can i link it with Synapse Analytics and use SPARK to query it? Can i have some links to good docs for starting guidance.
Related
Do any one know how to see the total space used and available in aws aurora schema, also the memory of the cluster within DB itself using queries. We are using AWS aurora psql and we don't have console, hence we want to try the same from queries like we will do for oracle.
This is most likely not doable using a SQL query in aurora, specifically the volume size. As an alternative, if you do have AWS CLI access, you should be able to query Cloudwatch metrics under "AWS/RDS" namespace to get these metrics in a more reliable and accurate manner. The ones you are interested in are "VolumeBytesUsed" and "CPUUtilization" if I'm not mistaken.
http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Aurora.Monitoring.html
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.
I have the event logs loaded in elasticsearch engine and I visualise it using Kibana. My event logs are actually stored in the Google Big Query table. Currently I am dumping the json files to a Google bucket and download it to a local drive. Then using logstash, I move the json files from the local drive to the elastic search engine.
Now, I am trying to automate the process by establishing the connection between google big query and elastic search. From what I have read, I understand that there is a output connector which sends the data from elastic search to Google big query but not vice versa. Just wondering whether I should upload the json file to a kubernete cluster and then establish the connection between the cluster and Elastic search engine.
Any help with this regard would be appreciated.
Although this solution may be a little complex, I suggest some solution that you use Google Storage Connector with ES-Hadoop. These two are very mature and used in production-grade by many great companies.
Logstash over a lot of pods on Kubernetes will be very expensive and - I think - not a very nice, resilient and scalable approach.
Apache Beam has connectors for BigQuery and Elastic Search, I would definitly perform this using DataFlow so you don´t need to implement a complex ETL and staging storage. You can read the data from BigQuery using BigQueryIO.Read.from (take a look to this if performance is important BigQueryIO Read vs fromQuery) and load it into ElasticSearch using ElasticsearchIO.write()
Refer this how read data from BigQuery Dataflow
https://github.com/GoogleCloudPlatform/professional-services/blob/master/examples/dataflow-bigquery-transpose/src/main/java/com/google/cloud/pso/pipeline/Pivot.java
Elastic Search indexing
https://github.com/GoogleCloudPlatform/professional-services/tree/master/examples/dataflow-elasticsearch-indexer
UPDATED 2019-06-24
Recently this year was release BigQuery Storage API which improve the parallelism to extract data from BigQuery and is natively supported by DataFlow. Refer to https://beam.apache.org/documentation/io/built-in/google-bigquery/#storage-api for more details.
From the documentation
The BigQuery Storage API allows you to directly access tables in BigQuery storage. As a result, your pipeline can read from BigQuery storage faster than previously possible.
I have recently worked on a similar pipeline. A workflow I would suggest would either use the mentioned Google storage connector, or other methods to read your json files into a spark job. You should be able to quickly and easily transform your data, and then use the elasticsearch-spark plugin to load that data into your Elasticsearch cluster.
You can use Google Cloud Dataproc or Cloud Dataflow to run and schedule your job.
As of 2021, there is a Dataflow template that allows a "GCP native" connection between BigQuery and ElasticSearch
More information here in a blog post by elastic.co
Further documentation and step by step process by google
I'm working on Windows command line as problems with Unix and firewalls prevent gsutil from working. I can read my Google Cloud Storage files and copy them over to other buckets (which I don't need to do). What I'm wondering is how to download them directly into HDFS (which I'm 'ssh'ing into)? Has anyone done this? Ideally this is part one, part two is creating Hive tables for the Google Cloud Storage data so we can use HiveQL and Pig.
You can use the Google Cloud Storage connector which provides an HDFS-API compatible interface to your data already in Google Cloud Storage, so you don't even need to copy it anywhere, just read from and write directly to your Google Cloud Storage buckets/objects.
Once you set up the connector, you can also copy data between HDFS and Google Cloud Storage with the hdfs tool, if necessary.
It is possible, to connect my Hadoop cluster to multiple Google Cloud Projects at once ?
I can easly use any Google Storage bucket in single Google Project via Google Cloud Storage Connector as explained in this thread Migrating 50TB data from local Hadoop cluster to Google Cloud Storage. But i can't find any documentation or example how to connect to two or more Google Cloud Project from single map-reduce job. Do You have any suggestion/trick ?
Thanks a lot.
Indeed, it is possible to connect your cluster to buckets from multiple different projects at once. Ultimately, if you're using the instructions for using a service-account keyfile, the GCS requests are performed on behalf of that service-account, which can be treated more-or-less like any other user. You can either add the service account email your-service-account-email#developer.gserviceaccount.com to all the different cloud projects owning buckets you want to process, using the permissions section of cloud.google.com/console and simply adding that email address like any other member, or you can set GCS-level access to add that service-account like any other user.