### Create Datasets and Tables

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Provides examples for creating new datasets and defining table schemas within those datasets.

```javascript
async function createDataset() {
  const datasetId = 'my_new_dataset';
  const options = { location: 'US' };
  const [dataset] = await bigquery.createDataset(datasetId, options);
  console.log(`Dataset ${dataset.id} created.`);
}

async function createTable() {
  const datasetId = 'my_dataset';
  const tableId = 'my_table';
  const schema = [
    {name: 'Name', type: 'STRING', mode: 'REQUIRED'},
    {name: 'Age', type: 'INTEGER'},
    {name: 'Weight', type: 'FLOAT'},
    {name: 'IsMagic', type: 'BOOLEAN'},
  ];
  const options = { schema: schema, location: 'US' };
  const [table] = await bigquery.dataset(datasetId).createTable(tableId, options);
  console.log(`Table ${table.id} created.`);
}
```

--------------------------------

### Install Google BigQuery Client Library

Source: https://github.com/googleapis/nodejs-bigquery/blob/main/README.md

Installs the official Google BigQuery Node.js client library via npm. This is a prerequisite for interacting with BigQuery services.

```bash
npm install @google-cloud/bigquery
```

--------------------------------

### Create a BigQuery Dataset

Source: https://github.com/googleapis/nodejs-bigquery/blob/main/README.md

Demonstrates how to initialize the BigQuery client and create a new dataset. It uses the asynchronous createDataset method and logs the resulting dataset ID.

```javascript
const {BigQuery} = require('@google-cloud/bigquery');

async function createDataset() {
  const bigqueryClient = new BigQuery();
  const [dataset] = await bigqueryClient.createDataset(datasetName);
  console.log(`Dataset ${dataset.id} created.`);
}
createDataset();
```

--------------------------------

### Initialize BigQuery Client

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Demonstrates how to instantiate the BigQuery client, which automatically handles authentication using Google Cloud Application Default Credentials.

```javascript
const {BigQuery} = require('@google-cloud/bigquery');
const bigquery = new BigQuery();
```

--------------------------------

### Execute SQL Queries

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Shows how to run a SQL query as a job and retrieve the results. This method is suitable for standard analytical queries.

```javascript
async function query() {
  const query = `SELECT name
    FROM `bigquery-public-data.usa_names.usa_1910_2013`
    WHERE state = 'TX'
    LIMIT 100`;
  const options = {
    query: query,
    location: 'US',
  };
  const [job] = await bigquery.createQueryJob(options);
  console.log(`Job ${job.id} started.`);
  const [rows] = await job.getQueryResults();
  rows.forEach(row => console.log(row));
}
```

--------------------------------

### Load Data from Cloud Storage

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Demonstrates how to trigger a load job to import data from a CSV file stored in Google Cloud Storage into a BigQuery table.

```javascript
async function loadData() {
  const datasetId = 'my_dataset';
  const tableId = 'my_table';
  const bucketName = 'my_bucket';
  const fileName = 'data.csv';
  const metadata = { sourceFormat: 'CSV', skipLeadingRows: 1, autodetect: true };
  const [job] = await bigquery.dataset(datasetId).table(tableId).load(storage.bucket(bucketName).file(fileName), metadata);
  console.log(`Job ${job.id} completed.`);
}
```

--------------------------------

### Copy BigQuery Tables

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Illustrates how to copy a source table to a destination table within BigQuery using the copy method.

```javascript
async function copyTable() {
  const srcDatasetId = 'source_dataset';
  const srcTableId = 'source_table';
  const destDatasetId = 'dest_dataset';
  const destTableId = 'dest_table';

  const [job] = await bigquery
    .dataset(srcDatasetId)
    .table(srcTableId)
    .copy(bigquery.dataset(destDatasetId).table(destTableId));

  console.log(`Job ${job.id} completed.`);
}
```

--------------------------------

### Manage and Monitor BigQuery Jobs

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Shows how to list, retrieve, monitor, and cancel BigQuery jobs. It includes event listeners for tracking job completion and error states.

```javascript
async function manageJob() {
  const [jobs] = await bigquery.getJobs();

  const job = bigquery.job('job_id');

  const [metadata] = await job.getMetadata();
  console.log('Job status:', metadata.status.state);

  await job.cancel();

  job.on('complete', metadata => {
    console.log('Job complete');
  });

  job.on('error', err => {
    console.error('Job error:', err);
  });
}
```

--------------------------------

### Manage Data Ingestion and Retrieval

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Covers streaming inserts for real-time data ingestion and standard row retrieval from existing tables.

```javascript
async function insertRowsAsStream() {
  const datasetId = 'my_dataset';
  const tableId = 'my_table';
  const rows = [{name: 'Tom', age: 30}, {name: 'Jane', age: 32}];
  await bigquery.dataset(datasetId).table(tableId).insert(rows);
  console.log(`Inserted ${rows.length} rows`);
}

async function getRows() {
  const datasetId = 'my_dataset';
  const tableId = 'my_table';
  const [rows] = await bigquery.dataset(datasetId).table(tableId).getRows();
  console.log('Rows:', rows);
}
```

--------------------------------

### Extract BigQuery Table to GCS

Source: https://github.com/googleapis/nodejs-bigquery/blob/main/README.md

Demonstrates how to export a BigQuery table to Google Cloud Storage. Supports various formats and compression options.

```javascript
async function extractTableToGCS(datasetId, tableId, bucketName, fileName) {
  const {BigQuery} = require('@google-cloud/bigquery');
  const bigquery = new BigQuery();
  const destinationUri = `gs://${bucketName}/${fileName}`;
  const file = bigquery.dataset(datasetId).table(tableId).file(destinationUri);
  const [job] = await file.extract();
  console.log(`Job ${job.id} started.`);
}
```

--------------------------------

### Export BigQuery Table to Cloud Storage

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Demonstrates how to export data from a BigQuery table to a CSV file in Google Cloud Storage. It uses the extract method with options for format and compression.

```javascript
async function extractTable() {
  const datasetId = 'my_dataset';
  const tableId = 'my_table';
  const bucketName = 'my_bucket';
  const fileName = 'export.csv';

  const options = {
    format: 'CSV',
    gzip: true,
  };

  const [job] = await bigquery
    .dataset(datasetId)
    .table(tableId)
    .extract(storage.bucket(bucketName).file(fileName), options);

  console.log(`Job ${job.id} completed.`);
}
```

--------------------------------

### Retrieve BigQuery Dataset Metadata

Source: https://github.com/googleapis/nodejs-bigquery/blob/main/README.md

Fetches metadata for a specific BigQuery dataset, including its properties and labels.

```javascript
async function getDataset(datasetId) {
  const {BigQuery} = require('@google-cloud/bigquery');
  const bigquery = new BigQuery();
  const [dataset] = await bigquery.dataset(datasetId).get();
  console.log('Dataset metadata:', dataset.metadata);
}
```

--------------------------------

### Manage Table-Level IAM Policies

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Provides methods to retrieve and update IAM policies for a specific BigQuery table, allowing for granular access control.

```javascript
async function manageIamPolicy() {
  const datasetId = 'my_dataset';
  const tableId = 'my_table';
  const table = bigquery.dataset(datasetId).table(tableId);

  const [policy] = await table.getIamPolicy();
  console.log('Current policy:', policy);

  const newPolicy = {
    bindings: [
      {
        role: 'roles/bigquery.dataViewer',
        members: ['user:viewer@example.com'],
      },
    ],
  };
  await table.setIamPolicy(newPolicy);
}
```

--------------------------------

### Stream Data into BigQuery Table

Source: https://github.com/googleapis/nodejs-bigquery/blob/main/README.md

Inserts rows into a BigQuery table using the streaming API, allowing for real-time data ingestion.

```javascript
async function insertRowsAsStream(datasetId, tableId, rows) {
  const {BigQuery} = require('@google-cloud/bigquery');
  const bigquery = new BigQuery();
  await bigquery.dataset(datasetId).table(tableId).insert(rows);
  console.log(`Inserted ${rows.length} rows.`);
}
```

--------------------------------

### Stream Query Results

Source: https://context7.com/googleapis/nodejs-bigquery/llms.txt

Utilizes a stream-based approach to process large query results efficiently without loading the entire result set into memory.

```javascript
function streamQueryResults() {
  const query = 'SELECT * FROM `my_dataset.my_table`';
  bigquery.createQueryStream(query)
    .on('data', row => console.log(row))
    .on('end', () => console.log('Stream complete'))
    .on('error', err => console.error('Error:', err));
}
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.