# First Batch Ingest

## Outcome

By the end of this page you will have imported CSV data into your `transcript` offline table and confirmed the rows are queryable.

## Prerequisites

* Completed [First table and schema](https://docs.pinot.apache.org/start-here/first-table-and-schema) -- the `transcript_OFFLINE` table must already exist.
* The sample CSV file at `/tmp/pinot-quick-start/rawdata/transcript.csv` from the previous step.
* For Docker users: set the `PINOT_VERSION` environment variable. See the [Version reference](https://docs.pinot.apache.org/start-here/pinot-versions) page.

## Steps

### 1. Understand batch ingestion

Batch ingestion reads data from files (CSV, JSON, Avro, Parquet, and others), converts them into Pinot segments, and pushes those segments to the cluster. A job specification YAML file tells Pinot where to find the input data, what format it is in, and where to send the finished segments.

### 2. Create the ingestion job spec

{% tabs %}
{% tab title="Local" %}
Create the file `/tmp/pinot-quick-start/batch-job-spec.yml`:

{% code title="/tmp/pinot-quick-start/batch-job-spec.yml" %}

```yaml
executionFrameworkSpec:
  name: 'standalone'
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
jobType: SegmentCreationAndTarPush
inputDirURI: '/tmp/pinot-quick-start/rawdata/'
includeFileNamePattern: 'glob:**/*.csv'
outputDirURI: '/tmp/pinot-quick-start/segments/'
overwriteOutput: true
pinotFSSpecs:
  - scheme: file
    className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
  dataFormat: 'csv'
  className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
  configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
tableSpec:
  tableName: 'transcript'
  schemaURI: 'http://localhost:9000/tables/transcript/schema'
  tableConfigURI: 'http://localhost:9000/tables/transcript'
pinotClusterSpecs:
  - controllerURI: 'http://localhost:9000'
```

{% endcode %}
{% endtab %}

{% tab title="Docker" %}
When running inside Docker, the ingestion job container must reach the controller by its Docker network hostname, not `localhost`. Create the file `/tmp/pinot-quick-start/batch-job-spec.yml`:

{% code title="/tmp/pinot-quick-start/batch-job-spec.yml" %}

```yaml
executionFrameworkSpec:
  name: 'standalone'
  segmentGenerationJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentGenerationJobRunner'
  segmentTarPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentTarPushJobRunner'
  segmentUriPushJobRunnerClassName: 'org.apache.pinot.plugin.ingestion.batch.standalone.SegmentUriPushJobRunner'
jobType: SegmentCreationAndTarPush
inputDirURI: '/tmp/pinot-quick-start/rawdata/'
includeFileNamePattern: 'glob:**/*.csv'
outputDirURI: '/tmp/pinot-quick-start/segments/'
overwriteOutput: true
pinotFSSpecs:
  - scheme: file
    className: org.apache.pinot.spi.filesystem.LocalPinotFS
recordReaderSpec:
  dataFormat: 'csv'
  className: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReader'
  configClassName: 'org.apache.pinot.plugin.inputformat.csv.CSVRecordReaderConfig'
tableSpec:
  tableName: 'transcript'
  schemaURI: 'http://pinot-controller:9000/tables/transcript/schema'
  tableConfigURI: 'http://pinot-controller:9000/tables/transcript'
pinotClusterSpecs:
  - controllerURI: 'http://pinot-controller:9000'
```

{% endcode %}

{% hint style="info" %}
Replace `pinot-controller` with the actual container name of your Pinot controller if you used a different name during setup.
{% endhint %}
{% endtab %}
{% endtabs %}

### 3. Run the ingestion job

{% tabs %}
{% tab title="Local" %}

```bash
bin/pinot-admin.sh LaunchDataIngestionJob \
    -jobSpecFile /tmp/pinot-quick-start/batch-job-spec.yml
```

{% endtab %}

{% tab title="Docker" %}

```bash
docker run --rm -ti \
    --network=pinot-demo \
    -v /tmp/pinot-quick-start:/tmp/pinot-quick-start \
    --name pinot-data-ingestion-job \
    apachepinot/pinot:${PINOT_VERSION} LaunchDataIngestionJob \
    -jobSpecFile /tmp/pinot-quick-start/batch-job-spec.yml
```

{% endtab %}
{% endtabs %}

The job reads the CSV file, builds a segment, and pushes it to the controller. You should see log output ending with a success message.

## Verify

1. Open the [Query Console](http://localhost:9000/query) in your browser.
2. Run the following query:

```sql
SELECT * FROM transcript
```

3. You should see **4 rows** returned, matching the CSV data you loaded:

| studentID | firstName | lastName | gender | subject | score | timestampInEpoch |
| --------- | --------- | -------- | ------ | ------- | ----- | ---------------- |
| 200       | Lucy      | Smith    | Female | Maths   | 3.8   | 1570863600000    |
| 200       | Lucy      | Smith    | Female | English | 3.5   | 1571036400000    |
| 201       | Bob       | King     | Male   | Maths   | 3.2   | 1571900400000    |
| 202       | Nick      | Young    | Male   | Physics | 3.6   | 1572418800000    |

## Next step

Continue to [First stream ingest](https://docs.pinot.apache.org/start-here/first-stream-ingest) to learn how to set up real-time ingestion from Kafka.