Sandbox benefits

Great question. Here’s a breakdown of top use cases for a Data Analyst working with:

1. BigQuery Sandbox, and
2. Raw Layer of a Data Lake (e.g., DataLake, GCS, or raw zone in a data lakehouse)

1. BigQuery Sandbox – Use Cases for Data Analysts

The BigQuery Sandbox is a free, no-credit-card-required environment, ideal for prototyping and learning. It has usage limits but supports real SQL capabilities.

Top Use Cases:

• Ad-hoc SQL Analysis
• Run quick queries against public datasets or connected sources for exploratory analysis.
• Data Cleaning and Transformation
• Use SQL to apply filters, remove duplicates, standardize formats (e.g., dates, currency).
• Data Joins Across Tables
• Combine datasets using JOIN to enrich or correlate data.
• Custom Metric Calculation
• Create derived metrics like conversion rates, retention, churn, etc.
• Visualization Prototyping
• Connect BigQuery Sandbox to tools like Looker Studio (free) for dashboard mockups.
• Query Optimization Practice
• Analyze execution plans and learn how to optimize SQL using partitioning, clustering, and caching.
• Public Dataset Exploration
• Leverage Google’s public datasets (e.g., COVID, Census, StackOverflow) for practice and insights.

2. Raw Layer of Data Lake – Use Cases for Data Analysts

The raw layer stores unprocessed, large-volume data — often in formats like JSON, Parquet, or CSV — usually on cloud storage (like GCS, S3, or Azure Data Lake).

Top Use Cases:

• Schema Discovery & Data Profiling
• Use tools like bq load, bq show, or data catalog to inspect structure, nulls, ranges, outliers.
• Data Ingestion Validation
• Check if all expected files landed, and validate record counts, file sizes, or timestamps.
• Raw-to-Curated Transformation
• Write SQL or Spark queries (depending on infra) to shape raw data into usable curated/clean tables.
• Change Detection
• Compare raw file drops over time for changes in volume, structure, or anomalies.
• Data Lineage & Traceability
• Understand source system behavior by inspecting raw logs or events (e.g., API payloads, user events).
• Staging for Snapshotting
• Pull in the raw data to BigQuery staging tables for snapshotting/archival analytics.

Combined Workflow Example:

1. Analyst pulls raw JSON logs from GCS into BigQuery temporary table.
2. Cleans and joins with dimension data in curated layer.
3. Creates KPIs and dashboards in Looker Studio using the sandbox or scheduled queries.

Would you like a visual diagram or cheat sheet to map these layers and use cases?

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Using big query schedule

To schedule queries in BigQuery, you use Scheduled Queries in the BigQuery UI, the bq command-line tool, or Terraform/API. Here’s how to do it via the UI (most common approach):

Step-by-Step: Schedule a Query in BigQuery UI

1. Open BigQuery Console
2. Go to: https://console.cloud.google.com/bigquery
3. Write Your Query
4. Open the SQL editor and write your query (e.g., a snapshot insert query).
5. Click on “Schedule”
7. You’ll see a “Schedule” button just above the query editor.
8. Click it and then select “Create new scheduled query”.
9. 
   
10. Set Job Details
12. Name your job.
13. Choose Project and Location (typically US or EU).
14. 
    
15. Set Frequency
17. Choose from predefined intervals: Hourly, Daily, Weekly, Monthly, or Custom using cron expressions.
18. Example (daily at midnight): 0 0 * * *
19. 
    
20. Set Start Time
22. Define when the schedule should start.
23. 
    
24. 
    
25. Destination (optional)
27. If your query writes results to a table, you can configure the destination table and write behavior (e.g., append or overwrite).
28. 
    
29. 
    
30. Notification Settings (optional)
32. Set up email notifications or connect to Cloud Logging.
33. 
    
34. 
    
35. Save
37. Click Save to create the scheduled query.
38. 
    
39. 
    

Optional Tips

• Permissions Needed:
• You need bigquery.jobs.create permission (usually included in roles like BigQuery Admin or Editor).
• Monitor Jobs:
• Navigate to BigQuery > Scheduled Queries to view or manage existing schedules.

Would you like a CLI version (bq command) or cron-style examples?

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Pain points migration from oracle to bigquery

1. 

SQL Syntax Differences

• Oracle uses PL/SQL, while BigQuery uses standard SQL.
• Key differences in functions, procedures, windowing functions, and error handling often require manual rewriting.

2. 

Stored Procedures & Packages

• Oracle’s stored procedures, packages, and triggers are deeply embedded in workflows.
• BigQuery has limited support for procedural logic and doesn’t support packages like Oracle.

3. 

ETL/ELT Reengineering

• ETL processes built with Oracle-specific tools (e.g., Oracle Data Integrator) must be re-architected using tools compatible with BigQuery (e.g., Dataflow, Cloud Composer, dbt).

4. 

Data Type Mismatches

• Differences in data types (e.g., NUMBER, DATE, CLOB) between Oracle and BigQuery can cause data fidelity issues during migration.

5. 

Performance Tuning Shift

• Oracle relies on indexes, partitions, and hints, while BigQuery is serverless and relies on query optimization and table partitioning strategies.
• Performance tuning paradigms must be relearned.

6. 

Security and Access Control

• Oracle uses role-based access control and fine-grained access policies, which may not map 1:1 to BigQuery’s IAM model.
• Requires redesign of data governance policies.

7. 

Migration of Historical Data

• Large volumes of data, especially if not well-partitioned, can be costly and time-consuming to move.
• May require staging in Google Cloud Storage and batch load jobs.

8. 

Change Management & User Training

• Analysts and developers accustomed to Oracle tools need training in BigQuery and associated cloud-native tools.
• Business users may also need to adapt to new reporting workflows (e.g., from Oracle BI to Looker).

9. 

Integration with Legacy Systems

• Oracle often integrates tightly with legacy ERP systems and custom apps.
• BigQuery may require middleware or APIs to maintain similar integrations.

10. 

Cost Management

• BigQuery uses pay-per-query pricing, which is very different from Oracle’s licensing model.
• Lack of understanding of cost controls (e.g., materialized views, partitions, byte limits) can lead to unexpected costs.

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Comparison Partition vs Cluster vs Shard

Here’s a detailed comparison matrix and use-case list for Partitioned Tables, Clustered Tables, and Sharded Tables in BigQuery. It covers factors like cost, performance, and trade-offs:

Comparison Matrix

Factor Partitioned Tables Clustered Tables Sharded Tables

Definition Divides a table into logical segments (partitions) based on a column (e.g., DATE or INTEGER). Organizes data within the table into sorted blocks based on one or more columns. Splits data into multiple physical tables (e.g., table_2025, table_2026).

Data Organization Data is stored by partition column (e.g., daily or monthly). Data within the table is clustered and sorted by the specified column(s). Data is stored in completely separate tables.

Supported Columns DATE, TIMESTAMP, DATETIME, INTEGER (for range partitions). Any column type (STRING, DATE, INTEGER, etc.). No restrictions; data is stored in separate tables.

Performance Query performance improves significantly when partition filters are used. Query performance improves for clustered column filters but requires a full table scan if filters are missing. Query performance is good when targeting specific shards but degrades with multiple shards.

Query Cost Costs are lower when partition filters are used (scans only relevant partitions). Costs are lower for clustered column filters, but full table scans cost more. Costs are higher for queries spanning multiple shards.

Storage Cost Single table, optimized for storage efficiency. Single table, efficient storage with clustering metadata overhead. Higher storage costs due to multiple tables.

Scalability Automatically adds partitions as new data arrives. Automatically handles clustering as new data arrives. Requires manual table creation/management for new shards.

Ease of Maintenance Easy to maintain; no manual intervention needed. Easy to maintain; no manual intervention needed. High maintenance; requires creating and managing multiple tables.

Trade-offs Optimized for large datasets with specific partitioning needs (e.g., time-series data). Best for tables with secondary filtering needs (e.g., on a STRING column after partitioning). Simple for small-scale datasets but becomes difficult to manage at scale.

Best Use Case Time-series or range-based data (e.g., logs, analytics data by date). Tables frequently queried with specific column filters (e.g., customer_id). Small datasets that naturally divide into discrete tables (e.g., annual reports).

Use Case List

1. Partitioned Tables

• Best For:

• Large, time-series datasets (e.g., logs, IoT data, analytics data).

• Queries that filter on date or range (e.g., WHERE date >= '2025-01-01' AND date <= '2025-01-31').

• Advantages:

• Optimized query performance with partition filters.

• Lower query costs since only relevant partitions are scanned.

• Scales automatically without manual intervention.

• Trade-offs:

• Limited to DATE, TIMESTAMP, DATETIME, or INTEGER columns for partitioning.

• Requires careful design to avoid too many small partitions (e.g., daily granularity for low-volume datasets).

• Example:

• A web analytics table partitioned by DATE to store daily user activity.

2. Clustered Tables

• Best For:

• Non-time-series data where queries filter on specific columns (e.g., user_id, region, product_id).

• Complementing partitioned tables for multi-dimensional filtering.

• Advantages:

• Improved query performance for columns used in clustering.

• No need to create or manage additional tables.

• Works with all column types, including STRING.

• Trade-offs:

• Full table scans occur if clustering filters are not applied.

• Clustering works best with frequently queried columns.

• Example:

• A sales data table clustered by region and product_id for optimized filtering.

3. Sharded Tables

• Best For:

• Small datasets with distinct natural separations (e.g., annual or region-specific data).

• Use cases with low query frequency where table-level granularity is acceptable.

• Advantages:

• Simple design for small datasets.

• No constraints on partitioning or clustering column types.

• Trade-offs:

• High maintenance: must manually create and manage new tables as data grows.

• Querying across multiple shards is expensive and slow.

• Does not leverage BigQuery’s native partitioning/clustering optimizations.

• Example:

• Annual billing reports stored as billing_2023, billing_2024, etc.

Trade-Offs Summary

1. Partitioned Tables:

• Optimal for date or range-based queries.

• Lower cost and maintenance for large datasets.

2. Clustered Tables:

• Great for secondary filters and multi-dimensional queries.

• Requires careful selection of clustering columns.

3. Sharded Tables:

• Simple but inefficient for large-scale datasets.

• High maintenance and expensive for queries spanning multiple tables.

Would you like help designing a schema or selecting the right strategy for your specific dataset?

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Partitioning existing tables

No, you cannot directly add partitions to an existing non-partitioned table in BigQuery. BigQuery does not support converting an existing non-partitioned table into a partitioned table or adding partitions to a table that wasn’t originally created with partitioning.

However, there are workarounds to achieve a similar result:

Workaround 1: Create a New Partitioned Table and Migrate Data

1. Create a New Partitioned Table: Define the partitioning scheme you need.

2. Insert Data from the Old Table: Use a SELECT query to copy data from the existing non-partitioned table to the new partitioned table.

Example:

-- Step 1: Create a new partitioned table

CREATE TABLE `project_id.dataset_id.new_table`

PARTITION BY DATE(timestamp_column) -- Replace with your partition column

AS

SELECT * FROM `project_id.dataset_id.old_table`;

3. Update your application or queries to use the new table.

Workaround 2: Use Clustering Instead of Partitioning

If you can’t migrate data, consider clustering the existing table. Clustering optimizes query performance and does not require recreating the table. Clustering can be done on multiple columns, including STRING, DATE, or INTEGER.

Example:

-- Create a clustered table

CREATE TABLE `project_id.dataset_id.new_table`

CLUSTER BY column_name -- Replace with the column(s) to cluster by

AS

SELECT * FROM `project_id.dataset_id.old_table`;

Workaround 3: Add Partitions to an Existing Partitioned Table

If the table is already partitioned (e.g., by date or range), BigQuery will automatically add partitions as new data arrives that falls outside the existing partitions. You don’t need to explicitly define additional partitions.

Example:

# Append new data to an existing partitioned table

from google.cloud import bigquery

import pandas as pd

client = bigquery.Client()

# Sample data

data = {"partition_column": ["2025-01-01"], "data_column": [100]}

df = pd.DataFrame(data)

# Load data into the table

table_id = "project_id.dataset_id.partitioned_table"

job_config = bigquery.LoadJobConfig(

  write_disposition="WRITE_APPEND",

  time_partitioning=bigquery.TimePartitioning(field="partition_column")

)

job = client.load_table_from_dataframe(df, table_id, job_config=job_config)

job.result()

Workaround 4: Use Sharded Tables Instead of Partitioning

If you can’t migrate or modify your existing table, you can implement sharded tables (e.g., table_2025, table_2026) to emulate partitioning.

Key Considerations

• Migrating data to a partitioned table is better for performance and cost efficiency, especially for large datasets.

• Clustering works well when partitioning isn’t feasible.

• Sharded tables can become difficult to manage over time.

Let me know if you want assistance with any specific approach!

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Clustering in bigquery to reduce filter cost

No, BigQuery does not support partitioning on string columns directly. Partitioning in BigQuery is limited to:

1. Time Partitioning:

• Based on DATE, TIMESTAMP, or DATETIME columns.

2. Integer Range Partitioning:

• Based on integer columns with a defined range.

Workaround for Partitioning on String Values

If you need to partition data based on string values, you can use one of these approaches:

1. Use Clustered Tables

BigQuery allows clustering on string columns. Clustering organizes data based on specified columns, improving query performance for those columns. While it’s not partitioning, it serves a similar purpose for filtering.

Example:

from google.cloud import bigquery

# Define table configuration with clustering

table = bigquery.Table("your-project-id.your-dataset-id.your-table-id")

table.clustering_fields = ["string_column"] # Specify string column for clustering

# Create the table

client = bigquery.Client()

client.create_table(table)

print(f"Table {table.table_id} created with clustering.")

2. Map Strings to Integers

You can map your string values to integers and use integer range partitioning.

Example:

If you have strings like ["A", "B", "C"], you can map them to integers [1, 2, 3]. Then use integer range partitioning on the mapped column.

# Mapping string to integer before loading into BigQuery

data = {

  "partition_column": [1, 2, 3], # Mapped integers

  "original_column": ["A", "B", "C"]

}

df = pd.DataFrame(data)

3. Use a Pseudo-Partition

Instead of native partitioning, add a STRING column to represent categories and filter the data in queries. This approach does not provide the storage and query optimization benefits of native partitioning.

Example:

SELECT * 

FROM `your-project-id.your-dataset-id.your-table-id`

WHERE string_column = "desired_value"

4. Use a DATE-Based Proxy

If string values correspond to dates (e.g., year-month), you can convert them into DATE format and use time partitioning.

Example:

df['partition_column'] = pd.to_datetime(df['string_column'], format="%Y-%m")

Key Considerations:

• Performance: Native partitioning is more efficient than pseudo-partitions.

• Cost: Filtering by string without clustering may increase query costs.

• Schema Design: Choose an approach that aligns with your query patterns.

Let me know if you’d like help implementing one of these approaches!

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Partitioning in BigQuery

When appending data to a partitioned table in BigQuery using Python and a DataFrame, you can specify the partition to which the data should be written. Here’s how you can do it step by step:

Prerequisites

1. Install the required libraries:

pip install google-cloud-bigquery pandas

2. Ensure your BigQuery table is partitioned (e.g., by date or integer range).

Code Example

Here’s an example of appending a DataFrame to a BigQuery partitioned table:

from google.cloud import bigquery

import pandas as pd

# Set up BigQuery client

client = bigquery.Client()

# Your project and dataset details

project_id = "your-project-id"

dataset_id = "your-dataset-id"

table_id = "your-table-id" # Replace with your table name

# Full table ID (project.dataset.table)

full_table_id = f"{project_id}.{dataset_id}.{table_id}"

# Sample DataFrame to append

data = {

  "partition_column": ["2025-01-01", "2025-01-02"], # Partition column (e.g., DATE)

  "data_column": [100, 200] # Other columns

}

df = pd.DataFrame(data)

# Define job configuration

job_config = bigquery.LoadJobConfig(

  write_disposition=bigquery.WriteDisposition.WRITE_APPEND, # Append data

  schema_update_options=[

    bigquery.SchemaUpdateOption.ALLOW_FIELD_ADDITION # If needed

  ],

  time_partitioning=bigquery.TimePartitioning( # Specify time partitioning

    field="partition_column" # The column used for partitioning

  ),

)

# Load DataFrame into BigQuery

job = client.load_table_from_dataframe(df, full_table_id, job_config=job_config)

job.result() # Wait for the job to complete

# Print success message

print(f"Data appended to table: {full_table_id}")

Key Points:

1. Partition Column: Ensure that the partition_column in your DataFrame matches the partitioning column of the BigQuery table.

2. Job Configuration:

• Use bigquery.TimePartitioning to specify the partition column if the table is time-partitioned.

• Use bigquery.RangePartitioning for range-partitioned tables.

3. Write Disposition: Set WRITE_APPEND to append data to the table.

4. Schema Update: If the schema changes (e.g., new columns), include SchemaUpdateOption.ALLOW_FIELD_ADDITION.

Partition Types:

• Time Partitioning: Based on a DATE or TIMESTAMP column.

• Range Partitioning: Based on an integer column.

If your table uses integer range partitions, adjust the configuration:

range_partitioning = bigquery.RangePartitioning(

  field="partition_column", # Integer column

  range_=bigquery.PartitionRange(start=0, end=1000, interval=100),

)

Common Errors and Solutions:

• Partition Not Found: Ensure the partition_column values in the DataFrame are compatible with the table’s partitioning.

• Schema Mismatch: Ensure the DataFrame columns match the table schema. Use explicit casting if needed.

Let me know if you need help with a specific part!

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Bigquery error

The error “SSL: CERTIFICATE_VERIFY_FAILED” indicates that Python is unable to verify the SSL certificate for the Google Cloud Storage or BigQuery endpoint. This is typically caused by a problem with your local SSL setup or missing CA certificates.

Here’s how to troubleshoot and fix this issue:

1. Verify Python Environment SSL Certificates

Ensure your Python environment has access to valid CA certificates.

• Anaconda Users: Run the following command to update certificates:

conda install -c anaconda certifi

• Non-Anaconda Users: If you’re using the standard Python installation, update the certifi package:

pip install --upgrade certifi

2. Set SSL Certificates Path (If Necessary)

Sometimes Python doesn’t locate the certificates correctly. Set the REQUESTS_CA_BUNDLE or SSL_CERT_FILE environment variable to the location of your certificates:

• Locate your certifi certificates:

import certifi

print(certifi.where())

This will print the path to the CA certificates file.

• Set the environment variable:

export REQUESTS_CA_BUNDLE=/path/to/your/certifi/cacert.pem

For Windows, you can set this variable in PowerShell:

$env:REQUESTS_CA_BUNDLE = "C:\path\to\certifi\cacert.pem"

3. Update Google Cloud Libraries

Make sure you are using the latest version of google-cloud-storage and google-cloud-bigquery libraries, as they often include fixes for SSL issues:

pip install --upgrade google-cloud-storage google-cloud-bigquery

4. Test Internet Access for SSL Verification

Run this test to confirm if your SSL verification works:

import requests

response = requests.get("https://www.google.com")

print(response.status_code)

If this fails, it confirms a broader SSL issue on your system.

5. Disable SSL Verification (Temporary Solution)

This is not recommended for production but can be used to bypass SSL verification for testing:

• Using the google-cloud-storage library:

from google.cloud import storage

from google.auth.transport.requests import Request

client = storage.Client(client_options={"api_endpoint": "https://storage.googleapis.com"})

session = Request()

session.verify = False

• Globally Disable SSL Verification: (Not recommended)

export PYTHONHTTPSVERIFY=0

6. Check Local Network Restrictions

If the issue persists, ensure that your local network or firewall is not blocking Google’s SSL certificates. You may need to:

• Use a different network or VPN.

• Check if the corporate firewall is intercepting HTTPS traffic and provide the proper certificates.

7. Alternative Debugging Steps

• Run on a Different Machine/Environment: Try executing the script on a different machine to rule out environment-specific issues.

• Reinstall Python or Anaconda: If the issue remains unresolved, consider reinstalling Python or your Anaconda environment to refresh SSL configurations.

Let me know if further assistance is required!

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Bigquery loading data error

The error “ValueError: Invalid call for scalar access (getting)” typically occurs in Python when working with Pandas, often because the code is trying to access a scalar value from a DataFrame or Series in an invalid way. In the context of BigQuery, this issue can arise when handling dataframes that are being uploaded to BigQuery.

Here’s a step-by-step guide to resolve the issue:

1. Identify the Problematic Line

• From your traceback:

return series.at[first_valid_index]

ValueError: Invalid call for scalar access (getting)

The error is triggered in the Pandas indexing logic. The issue likely comes from trying to access a value in an empty or invalid Series/Index.

2. Possible Causes

• Empty DataFrame/Series: The DataFrame being passed to BigQuery might have an empty column or row.

• Incorrect Indexing: There might be an invalid attempt to use .at[] or .iloc[] on a Series/Index that doesn’t exist.

• Schema Mismatch: The BigQuery schema might not match the DataFrame schema, causing unexpected issues during transformation.

3. Debugging Steps

• Inspect the DataFrame: Add a debug statement to examine the DataFrame (df) before it’s uploaded:

print(df.info())

print(df.head())

Look for empty rows, columns, or invalid data types.

• Check Data Validity:

Ensure there’s valid data in the columns being accessed or uploaded:

if df.empty:

  print("DataFrame is empty. Check your input data or preprocessing steps.")

• Validate BigQuery Schema: Ensure that the DataFrame column names and data types match the BigQuery table schema.

4. Fixing the Issue

• Handle Empty Series:

Replace the problematic indexing operation with a check for empty or invalid Series:

if not series.empty:

  result = series.at[first_valid_index]

else:

  raise ValueError("Series is empty; cannot access scalar value.")

• Clean the DataFrame: Drop empty rows/columns before uploading:

df = df.dropna(how='all') # Remove rows/columns with all values as NaN

• Modify Upload Logic: If the issue lies in the load_table_from_dataframe method, ensure the data is valid before sending it to BigQuery.

5. Additional Suggestions

• Test with Subset Data: Start with a smaller, validated DataFrame to isolate the issue.

• Update Libraries: Ensure you’re using the latest versions of google-cloud-bigquery and pandas:

pip install --upgrade google-cloud-bigquery pandas

If you share more details about the DataFrame or the BigQuery schema, I can help refine the solution further!

From Blogger iPhone client

Big query information schema

In BigQuery, INFORMATION_SCHEMA views provide metadata about datasets, tables, columns, and other database objects within a project. These views are useful for querying system-related information, such as table sizes, schema details, jobs, and user privileges.

Here are the main types of INFORMATION_SCHEMA in BigQuery:

1. Dataset-Level INFORMATION_SCHEMA Views

These views provide information about all the objects within a specific dataset.

• INFORMATION_SCHEMA.TABLES: Contains metadata about all tables and views in a dataset.

• Example: Get the size and creation time of tables.

• Columns include table_name, table_type, creation_time, size_bytes.

• INFORMATION_SCHEMA.COLUMNS: Contains metadata about columns in the tables and views within a dataset.

• Example: List column names and data types.

• Columns include table_name, column_name, data_type, is_nullable.

• INFORMATION_SCHEMA.ROUTINES: Provides details about user-defined functions (UDFs) and stored procedures.

• Example: Get the definition of stored routines.

• Columns include routine_name, routine_type, routine_body.

• INFORMATION_SCHEMA.PARTITIONS: Shows information about table partitions.

• Example: List the partitions of a partitioned table.

• Columns include table_name, partition_id, partition_creation_time.

• INFORMATION_SCHEMA.JOBS: Provides information about all jobs (queries, loads, etc.) run within a dataset.

• Example: View recent query jobs and their statuses.

• Columns include job_id, user_email, job_type, creation_time, state.

• INFORMATION_SCHEMA.TABLE_OPTIONS: Lists the table options such as expiration times for each table in a dataset.

• Example: Check if tables have expiration times set.

• Columns include table_name, option_name, option_value.

2. Project-Level INFORMATION_SCHEMA Views

These views provide metadata across the entire project.

• INFORMATION_SCHEMA.SCHEMATA: Lists all datasets in a project.

• Example: Get all dataset names in a project.

• Columns include schema_name (dataset name), location.

• INFORMATION_SCHEMA.JOBS_BY_PROJECT: Provides details on all jobs across the entire project.

• Example: Track the status of jobs like queries, loads, and exports.

• Columns include job_id, user_email, job_type, creation_time, state.

• INFORMATION_SCHEMA.JOBS_TIMELINE_BY_PROJECT: Offers a more detailed timeline of job execution across the project.

• Example: Analyze when jobs start, run, and complete.

• Columns include job_id, job_duration, bytes_processed.

3. Region-Specific INFORMATION_SCHEMA Views

These views are similar to project-level views but are scoped to a specific region within a project. They are useful for analyzing jobs or datasets based on their geographic location.

• INFORMATION_SCHEMA.JOBS_BY_REGION: Lists all jobs in a project for a specific region.

• Example: Monitor job activity for a specific region.

• Columns include job_id, location, job_type, state.

• INFORMATION_SCHEMA.JOBS_TIMELINE_BY_REGION: Shows detailed job timelines within a specific region.

• Example: Analyze the performance and duration of jobs region-wise.

• Columns include job_id, job_duration, bytes_processed.

4. Reservation INFORMATION_SCHEMA Views

These views provide information about slots and reservations, useful for managing BigQuery slots and resource reservations.

• INFORMATION_SCHEMA.RESERVATIONS: Lists information about all slot reservations in a project.

• Example: List all reservations and their usage.

• Columns include reservation_name, slot_count.

• INFORMATION_SCHEMA.ASSIGNMENTS: Shows assignments of reservations to projects.

• Example: Find out how slot reservations are assigned to different projects.

• Columns include project_id, reservation_name.

5. Other Special Information Schema Views

These views provide metadata about specialized objects like policies or capacity commitments.

• INFORMATION_SCHEMA.POLICY_TAGS: Lists policy tags (for data governance and classification) for tables in a dataset.

• Example: List all policy tags attached to columns for data classification.

• Columns include policy_tag, column_name, table_name.

• INFORMATION_SCHEMA.CAPACITY_COMMITMENTS: Provides details about capacity commitments for slots in a project.

• Example: Check the slot capacity commitments for a project.

• Columns include commitment_id, slot_count, plan.

Usage of INFORMATION_SCHEMA Views

• Query Dataset Metadata: Use INFORMATION_SCHEMA.TABLES or INFORMATION_SCHEMA.COLUMNS to get information about the structure of datasets and tables.

• Track Jobs: Use INFORMATION_SCHEMA.JOBS to

From Blogger iPhone client

Egress Cost between Bigquery and tableau on the Azure

When connecting **BigQuery** to **Tableau** on an **Azure instance**, the cost primarily associated with data egress (transferring data from Google Cloud to Azure). However, there are several other potential costs involved, which can vary depending on your architecture and usage patterns. Here’s a breakdown of the different types of costs:

### 1. **BigQuery Costs**

  - **Query Processing Costs**: BigQuery charges based on the amount of data processed by your SQL queries. Even if data is being pulled to Tableau, if a query is run on BigQuery, you will be charged for the data scanned by the query.

   - **On-Demand Pricing**: Pay per terabyte of data processed.

   - **Flat-Rate Pricing**: Fixed monthly fee for a dedicated slot capacity, which can be cost-effective for heavy usage.

  - **Storage Costs**:

   - **Active Storage**: The cost of storing your data in BigQuery.

   - **Long-Term Storage**: Discounted rates for data that hasn't been updated in 90 days.

  - **Streaming Insert Costs** (if applicable): If you are streaming data into BigQuery in real-time, this incurs additional costs.

### 2. **Data Egress Costs (Google Cloud to Azure)**

  - **Data Egress Fees**: Transferring data from Google Cloud (where BigQuery resides) to another cloud provider (Azure) incurs egress fees. These are based on the amount of data transferred out of Google Cloud to the external service.

   - **Data Transfer Pricing**: Charges vary depending on the region from which data is being transferred. For example, transferring data between continents may be more expensive than transferring data within the same region.

### 3. **Azure Costs**

  - **Virtual Machine (VM) Costs**: Running Tableau on an Azure instance typically involves virtual machine (VM) costs. The pricing depends on the VM's type, size, and how long it runs.

   - **Compute Costs**: You are charged for the processing power consumed by the VM.

   - **Storage Costs**: Any storage used for the VM's operating system or application data will also incur costs.

  - **Bandwidth Costs**: Azure charges for outbound data transfers, although inbound data (such as data coming from BigQuery) is generally free. However, if your Tableau instance is accessed by users over the internet, additional bandwidth costs may apply.

### 4. **Tableau Licensing Costs**

  - **Tableau Server or Tableau Online**: If you are running Tableau on Azure, you need to factor in the cost of a **Tableau Server** license (for on-premise or cloud deployment) or **Tableau Online** (Tableau's fully hosted service). These costs are separate from the infrastructure costs.

   - **Core-Based Licensing**: Based on the number of cores available on your server instance.

   - **User-Based Licensing**: Based on the number of users accessing Tableau.

### 5. **Network and Security Costs**

  - **Virtual Network (VNet) or VPN Costs**: If you have a private network connection between Azure and your data sources (e.g., using a VPN or an ExpressRoute connection), there may be additional costs for maintaining this setup.

  - **Firewall and Security Services**: Azure and Google Cloud offer security services such as firewalls, security groups, and monitoring services that could incur costs based on usage.

### Summary of Cost Types:

1. **BigQuery Costs**:

  - Query Processing

  - Storage (Active & Long-Term)

  - Streaming Inserts (if applicable)

2. **Data Egress**:

  - Google Cloud to Azure Egress Fees

3. **Azure Costs**:

  - VM Costs (Compute & Storage)

  - Bandwidth (Outbound)

4. **Tableau Costs**:

  - Licensing (Server or Online)

5. **Network/Security** (if applicable):

  - VNet or VPN Costs

  - Security and Monitoring Services

To estimate your total cost, you would need to account for each of these factors based on your specific usage scenario.

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