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EDU-1502: Adds bigQuery page
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---
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title: BigQuery rule
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meta_description: "Stream realtime event data from Ably into Google BigQuery using the Firehose BigQuery rule. Configure, and analyze your data efficiently."
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---
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Stream events published to Ably directly into a table in "BigQuery":https://cloud.google.com/bigquery?utm_source=google&utm_medium=cpc&utm_campaign=emea-es-all-en-dr-bkws-all-all-trial-e-gcp-1707574&utm_content=text-ad-none-any-dev_c-cre_574561258287-adgp_Hybrid+%7C+BKWS+-+EXA+%7C+Txt+-+Data+Analytics+-+BigQuery+-+v1-kwid_43700072692462237-kwd-12297987241-userloc_1005419&utm_term=kw_big+query-net_g-plac_&&gad_source=1&gclid=Cj0KCQiAwtu9BhC8ARIsAI9JHanslQbN6f8Ho6rvEvozknlBMbqaea0s6ILK-VA9YpQhRr_IUrVz6rYaAtXeEALw_wcB&gclsrc=aw.ds&hl=en for analytical or archival purposes. General use cases include:
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* Realtime analytics on message data.
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* Centralized storage for raw event data, enabling downstream processing.
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* Historical auditing of messages.
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<aside data-type='note'>
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<p>Ably's BigQuery integration rule for "Firehose":/integrations/streaming is in development status.</p>
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</aside>
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h3(#create-rule). Create a BigQuery rule
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Set up the necessary BigQuery resources, permissions, and authentication to enable Ably to securely write data to a BigQuery table:
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* Create or select a BigQuery dataset in the Google Cloud Console.
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* Create a BigQuery table in that dataset:
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** Use the "JSON schema":#schema.
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** For large datasets, partition the table by ingestion time, with daily partitioning recommended for optimal performance.
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* Create a Google Cloud Platform (GCP) "service account":https://cloud.google.com/iam/docs/service-accounts-create?utm_source=chatgpt.com with the minimal required BigQuery permissions.
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* Grant the service account table-level access control to allow access to the specific table.
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** @bigquery.tables.get@: to read table metadata.
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** @bigquery.tables.updateData@: to insert records.
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* Generate and securely store the JSON key file for the service account.
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** Ably requires this key file to authenticate and write data to your table.
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h3(#settings). BigQuery rule settings
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The following explains the components of the BigQuery rule settings:
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|_. Section |_. Purpose |
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| *Source* | Defines the type of event(s) for delivery. |
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| *Channel filter* | A regular expression to filter which channels to capture. Only events on channels matching this regex are streamed into BigQuery. |
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| *Table* | The full destination table path in BigQuery, typically in the format @project_id.dataset_id.table_id@. |
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| *Service account Key* | A JSON key file Ably uses to authenticate with Google Cloud. You must upload or provide the contents of this key file. |
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| *Partitioning* | _(Optional)_ The table must be created with the desired partitioning settings in BigQuery before making the rule in Ably. |
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| *Advanced settings* | Any additional configuration or custom fields relevant to your BigQuery setup (for future enhancements). |
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h4(#dashboard). Create a BigQuery rule in the dashboard
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The following steps to create a BigQuery rule using the Ably dashboard:
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* Log in to the "Ably dashboard":https://ably.com/accounts/any and select the application you want to stream data from.
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* Navigate to the *Integrations* tab.
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* Click *New integration rule*.
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* Select *Firehose*.
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* Choose *BigQuery* from the list of available Firehose integrations.
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* Configure the rule settings as described below.Then, click *Create*.
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h4(#api-rule). Create a BigQuery rule using the Control API
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The following steps to create a BigQuery rule using the "Control API:":https://ably.com/docs/api#control-api
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* Using the required "rules":/control-api#examples-rules to specify the following parameters:
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** @ruleType@: Set this to "bigquery" to define the rule as a BigQuery integration.
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** destinationTable: Specify the BigQuery table where the data will be stored.
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** @serviceAccountCredentials@: Provide the necessary GCP service account JSON key to authenticate and authorize data insertion.
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** @channelFilter@ (optional): Use a regular expression to apply the rule to specific channels.
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** @format@ (optional): Define the data format based on how you want messages to be structured.
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* Make an HTTP request to the Control API to create the rule.
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h3(#schema). JSON Schema
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You can run queries directly against the Ably-managed BigQuery table. For example, if the message payloads are stored as raw JSON in the data column, you can parse them using the following query:
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```[json]
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{
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“name”: “id”,
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“type”: “STRING”,
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“mode”: “REQUIRED”,
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“description”: “Unique ID assigned by Ably to this message. Can optionally be assigned by the client.”
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}
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```
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h3(#queries). Direct queries
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Run queries directly against the Ably-managed table. For instance, to parse JSON payloads stored in @data@:
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```[sql]
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SELECT
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PARSE_JSON(CAST(data AS STRING)) AS parsed_payload
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FROM project_id.dataset_id.table_id
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WHERE channel = “my-channel”
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```
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The following explains the components of the query:
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|_. Query Function |_. Purpose |
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| @CAST(data AS STRING)@ | Converts the data column from BYTES (if applicable) into a STRING format. |
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| @PARSE_JSON(…)@ | Parses the string into a structured JSON object for easier querying. |
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| @WHERE channel = “my-channel”@ | Filters results to retrieve messages only from a specific Ably channel. |
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<aside data-type='note'>
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<p>Parsing JSON at query time can be computationally expensive for large datasets. If your queries need frequent JSON parsing, consider pre-processing and storing structured fields in a secondary table using an ETL pipeline for better performance.</p>
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</aside>
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h4(#etl). Extract, Transform, Load (ETL)
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ETL is recommended for large-scale analytics and performance optimization, ensuring data is structured, deduplicated, and efficiently stored for querying. Transform raw data (JSON or BYTES) into a more structured format, remove duplicates, and write it into a secondary table optimized for analytics:
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* Convert data from raw (BYTES/JSON) into structured columns for example geospatial data fields or numeric data types, for detailed analysis.
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* Write transformed records to a new optimized table tailored for query performance.
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* Deduplicate records using the unique ID field to ensure data integrity.
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* Automate the process using BigQuery scheduled queries or an external workflow to run transformations at regular intervals.
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