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# Estrazione di dati dal catalogo AWS Glue dati per l'analisi delle chiamate dell'SDK Amazon Chime
<a name="ca-data-model-queries"></a>

Usa queste query di esempio per estrarre e organizzare i dati nel catalogo di dati Glue per l'analisi delle chiamate dell'SDK Amazon Chime. 

**Nota**  
Per informazioni sulla connessione ad Amazon Athena e sull'interrogazione del catalogo dati Glue, consulta [Connessione ad Amazon Athena](https://docs.aws.amazon.com/athena/latest/ug/connect-with-odbc.html) con ODBC.

Espandi ogni sezione in base alle esigenze.

## Estrazione di valori dai metadati (tipo di dati STRING) nella tabella call\_analytics\_metadata
<a name="qry-insights-metadata"></a>

`call_analytics_metadata`ha il campo in formato stringa JSON. `metadata` Usa la [funzione json\_extract\_scalar](https://docs.aws.amazon.com/athena/latest/ug/extracting-data-from-JSON.html) in Athena per interrogare gli elementi di questa stringa.

```
SELECT
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID"
FROM 
    "GlueDatabaseName"."call_analytics_metadata"
```

## Interrogazione degli aggiornamenti SIPRecMetadata nella tabella call\_analytics\_metadata
<a name="qry-insights-siprec-metadata"></a>

Il campo contiene il campo dei metadati in formato stringa JSON. `call_analytics_metadata` `metadata`ha un altro oggetto annidato chiamato`oneTimeMetadata`, questo oggetto contiene metadati SIPrec in formato XML originale e JSON trasformato. Usa la `json_extract_scalar` funzione in Athena per interrogare gli elementi di questa stringa.

```
SELECT
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID",
    json_extract_scalar(json_extract_scalar(metadata,'$.oneTimeMetadata'),'$.siprecMetadata') AS "siprec Metadata XML",
    json_extract_scalar(json_extract_scalar(metadata,'$.oneTimeMetadata'),'$.siprecMetadataJson') AS "Siprec Metadata JSON",
    json_extract_scalar(json_extract_scalar(metadata,'$.oneTimeMetadata'),'$.inviteHeaders') AS "Invite Headers"
FROM 
    "GlueDatabaseName"."call_analytics_metadata"
WHERE 
    callevent-type = "update";
```

## Estrazione di valori dai metadati (tipo di dati STRING) nella tabella call\_analytics\_recording\_metadata
<a name="qry-recording-metadata"></a>

`call_analytics_recording_metadata`ha il campo dei metadati in formato stringa JSON. Usa la [funzione json\_extract\_scalar](https://docs.aws.amazon.com/athena/latest/ug/extracting-data-from-JSON.html) in Athena per interrogare gli elementi di questa stringa.

```
SELECT
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID"
FROM 
    "GlueDatabaseName"."call_analytics_recording_metadata"
WHERE 
    detail-subtype = "Recording"
```

## Estrazione di valori dal dettaglio (tipo di dati STRUCT) nella tabella voice\_analytics\_status
<a name="qry-va-status"></a>

`voice_analytics_status`ha un campo di dettagli nel `struct` tipo di dati. L'esempio seguente mostra come interrogare un campo di tipo di `struct` dati:

```
SELECT
    detail.transactionId AS "Transaction ID",
    detail.voiceConnectorId AS "VoiceConnector ID",
    detail.siprecmetadata AS "Siprec Metadata",
    detail.inviteheaders AS "Invite Headers",
    detail.streamStartTime AS "Stream Start Time"
FROM 
    "GlueDatabaseName"."voice_analytics_status"
```

## Unire le tabelle voice\_analytics\_status e call\_analytics\_metadata
<a name="qry-join-va-meta"></a>

La seguente query di esempio unisce e: `call_analytics_metadata` `voice_analytics_status`

```
SELECT
    a.detail.transactionId AS "Transaction ID",
    a.detail.voiceConnectorId AS "VoiceConnector ID",
    a.detail.siprecmetadata AS "Siprec Metadata",
    a.detail.inviteheaders AS "Invite Headers",
    a.detail.streamStartTime AS "Stream Start Time"
    json_extract_scalar(b.metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(b.metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(b.metadata,'$.callId') AS "Call ID",
    json_extract_scalar(b.metadata,'$.direction') AS Direction
FROM 
    "GlueDatabaseName"."voice_analytics_status" a
INNER JOIN 
    "GlueDatabaseName"."call_analytics_metadata" b
ON a.detail.transactionId = json_extract_scalar(b.metadata,'$.transactionId')
```

## Estrazione delle trascrizioni dalla tabella transcribe\_call\_analytics\_post\_call
<a name="qry-transcribe-ca-post-call"></a>

transcribe\_call\_analytics\_post\_call ha un campo di trascrizione in formato struct con array annidati. Utilizzate la seguente query per rimuovere l'annidamento degli array:

```
SELECT 
    jobstatus,
    languagecode,
    IF(CARDINALITY(m.transcript)=0 OR CARDINALITY(m.transcript) IS NULL, NULL, e.transcript.id) AS utteranceId,
    IF(CARDINALITY(m.transcript)=0 OR CARDINALITY(m.transcript) IS NULL, NULL, e.transcript.content) AS transcript,
    accountid,
    channel,
    sessionid,
    contentmetadata.output AS "Redaction"
FROM 
    "GlueDatabaseName"."transcribe_call_analytics_post_call" m
CROSS JOIN UNNEST
    (IF(CARDINALITY(m.transcript)=0, ARRAY[NULL], transcript)) AS e(transcript)
```

## Unire le tabelle transcribe\_call\_analytics\_post\_call e call\_analytics\_metadata
<a name="qry-va-status"></a>

La seguente query unisce transcribe\_call\_analytics\_post\_call e call\_analytics\_metadata:

```
WITH metadata AS(
  SELECT 
    from_iso8601_timestamp(time) AS "Timestamp",
    date_parse(date_format(from_iso8601_timestamp(time), '%m/%d/%Y %H:%i:%s') , '%m/%d/%Y %H:%i:%s') AS "DateTime",
    date_parse(date_format(from_iso8601_timestamp(time) , '%m/%d/%Y') , '%m/%d/%Y') AS "Date",
    date_format(from_iso8601_timestamp(time) , '%H:%i:%s')  AS "Time",
    mediainsightspipelineid,
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID",
    REGEXP_REPLACE(REGEXP_EXTRACT(json_extract_scalar(metadata,'$.oneTimeMetadata.s3RecordingUrl'), '[^/]+(?=\.[^.]+$)'), '\.wav$', '') AS "SessionID"
  FROM 
    "GlueDatabaseName"."call_analytics_metadata"
),
transcript_events AS(
  SELECT 
    jobstatus,
    languagecode,
    IF(CARDINALITY(m.transcript)=0 OR CARDINALITY(m.transcript) IS NULL, NULL, e.transcript.id) AS utteranceId,
    IF(CARDINALITY(m.transcript)=0 OR CARDINALITY(m.transcript) IS NULL, NULL, e.transcript.content) AS transcript,
    accountid,
    channel,
    sessionid,
    contentmetadata.output AS "Redaction"
  FROM 
    "GlueDatabaseName"."transcribe_call_analytics_post_call" m
  CROSS JOIN UNNEST
    (IF(CARDINALITY(m.transcript)=0, ARRAY[NULL], transcript)) AS e(transcript)
)
SELECT 
    jobstatus,
    languagecode,
    a.utteranceId,
    transcript,
    accountid,
    channel,
    a.sessionid,
    "Redaction"
    "Timestamp",
    "DateTime",
    "Date",
    "Time",
    mediainsightspipelineid,
    "To Number",
    "VoiceConnector ID",
    "From Number",
    "Call ID",
    Direction,
    "Transaction ID"
FROM 
    "GlueDatabaseName"."transcribe_call_analytics_post_call" a
LEFT JOIN 
    metadata b
ON 
    a.sessionid = b.SessionID
```

## Interrogazione degli URL degli oggetti multimediali per la registrazione delle chiamate di miglioramento vocale
<a name="qry-voice-enhancement-call-recording"></a>

La seguente query di esempio unisce l'URL: `Voice enhancement call recording`

```
SELECT 
    json_extract_scalar(metadata,'$.voiceConnectorId') AS "VoiceConnector ID",
    json_extract_scalar(metadata,'$.fromNumber') AS "From Number",
    json_extract_scalar(metadata,'$.toNumber') AS "To Number",
    json_extract_scalar(metadata,'$.callId') AS "Call ID",
    json_extract_scalar(metadata,'$.direction') AS Direction,
    json_extract_scalar(metadata,'$.transactionId') AS "Transaction ID",
    s3MediaObjectConsoleUrl
FROM
    {GlueDatabaseName}."call_analytics_recording_metadata"
WHERE
    detail-subtype = "VoiceEnhancement"
```