No-code pipeline · api4com → Amazon S3

Send data from api4com to Amazon S3

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From api4com to Amazon S3: managed, scheduled, no code.

Creating a pipeline that sends data from api4com to Amazon S3 data lakes takes only a few minutes with Kondado. And the whole integration from api4com to Amazon S3 is managed and executed by our platform. With Kondado, you can focus on extracting value from api4com data and combining it with other data in your Amazon S3 data lake

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Send api4com Data to Amazon S3 Automatically

Replicating your api4com voice communication data to Amazon S3 requires no engineering effort with Kondado. Simply connect your api4com account as a data source, select Amazon S3 as your destination, and configure your update schedule. Kondado handles the data extraction and loading automatically, delivering your call records and recharge data directly to your S3 buckets on a configurable schedule ranging from every 5 minutes to daily, ensuring your data lake stays current without manual intervention. The platform connects 80+ sources, allowing you to consolidate api4com alongside other business data for comprehensive analysis.

Once your api4com data lands in Amazon S3, you can analyze voice call costs, monitor recharge patterns, and build custom reports using Athena, Presto, or Dremio. This automated pipeline eliminates manual CSV exports and keeps your analytics infrastructure updated with fresh communication data, enabling your team to make data-driven decisions about telecommunication expenses and usage trends.

Kondado provides a direct integration between api4com and Amazon S3, automatically replicating Voice Calls and Recharge History pipelines to your S3 buckets on a configurable schedule ranging from every 5 minutes to daily, with no coding required.

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With the Voice Calls pipeline, you receive detailed records including caller information, duration, and call pricing, enabling cost analysis and usage optimization directly within your S3 data lake. The Recharge History pipeline delivers username, credit dates, values, and observations, allowing finance teams to track prepaid balance movements and reconcile telecommunication expenses. By having both communication activity and financial recharge data available in Amazon S3, you can join these datasets with other business information stored in your data lake to create comprehensive profitability analyses and operational dashboards without relying on manual data extracts. This automated flow supports analytics workflows in BigQuery, PostgreSQL, or direct query engines like Athena.

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Replicated to Amazon S3

api4com data available for Amazon S3

Tables Kondado writes into your Amazon S3, on a schedule you control.

available pipelines

extractable fields

Amazon S3

Destination

Available integrations

Voice Calls

Table provides details about calls, including fields such as 'from', 'to', 'duration', and 'call_price'.

Recharge History

Table contains information about recharges, highlighting fields such as 'username', 'credit_date', 'value', and 'obs'.

See all available data → Send api4com to Amazon S3 →

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How to send api4com data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect api4com Account

Authenticate your api4com credentials in Kondado by providing your API token and account details to establish the data source connection.

Configure S3 Destination

Enter your Amazon S3 bucket name and path details where you want voice call and recharge data stored, ensuring proper separation of storage and compute for your analytics workflow.

Select Pipelines and Schedule

Choose the Voice Calls and Recharge History pipelines, then set your preferred update frequency from every 5 minutes to daily to automate data replication to your data lake.

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Send data from api4com to other destinations

api4com > Looker Studio

api4com > Power BI

Other visualizations

api4com > Amazon S3

api4com > BigQuery

api4com > Excel

api4com > Google Sheets

api4com > MySQL

api4com > PostgreSQL

api4com > Redshift

api4com > SQL Server

Choose a tool to visualize your api4com data

If the software you need is not listed, drop us a messagem. You can use almost every tool

api4com > Airtable

api4com > Alteryx

api4com > Amazon Quicksight

api4com > BIMachine

api4com > Cortex

api4com > Data Studio

api4com > Geckoboard

api4com > Grafana

api4com > IBM Cognos Analytics

api4com > Klipfolio

api4com > Looker Studio

api4com > Looker

api4com > Looqbox

api4com > Metabase

api4com > Microstrategy

api4com > Pentaho

api4com > Power BI

api4com > Qlik Cloud Analytics

api4com > Qlik Sense

api4com > Qlik

api4com > QlikView

api4com > Redash

api4com > Superset

api4com > Tableau

api4com > BI TOTVS

api4com > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending api4com data to Amazon S3 automatically

How do I connect api4com to Amazon S3 without writing code? ▼

Kondado offers a no-code interface where you authenticate your api4com credentials, provide your Amazon S3 bucket details, and select which pipelines to replicate. The platform handles the API extraction and S3 loading automatically, requiring no Python scripts or engineering resources from your team.

What specific call details are available in the Voice Calls pipeline? ▼

The Voice Calls pipeline includes fields such as 'from', 'to', 'duration', and 'call_price', allowing you to analyze communication patterns and calculate exact telecommunication costs. This granular data supports detailed billing verification and usage trend analysis directly within your S3 environment.

How often does api4com data update in my S3 bucket? ▼

You can configure updates to run every 5 minutes, 15 minutes, hourly, or daily depending on your analytics requirements. This flexible scheduling ensures your BigQuery queries or Athena analyses always access the most recent call records and recharge information without overwhelming your API limits.

What file format does Kondado use when saving api4com data to S3? ▼

Data arrives in Amazon S3 in optimized file formats compatible with Athena, Presto, and Dremio, enabling immediate querying without additional transformation steps. This structure supports seamless integration with your existing data virtualization layer and analytics tools.

Can I combine api4com recharge data with Salesforce or other CRM data in S3? ▼

Yes, since Amazon S3 serves as a central data lake, you can join api4com Recharge History records with CRM data, marketing platforms, or financial systems that you also replicate to S3. This consolidation enables comprehensive customer profitability analysis and unified reporting across your entire technology stack.

Do I need to manually export CSV files from api4com before replicating to S3? ▼

No manual exports are necessary, as Kondado connects directly to the api4com API and pulls data automatically on your configured schedule. This eliminates the need for downloading spreadsheets or maintaining custom scripts to transfer voice communication records to your data lake.

What happens to historical api4com data when I first set up the pipeline? ▼

During initial setup, Kondado can retrieve historical records from api4com to populate your S3 bucket with past voice calls and recharge transactions. This backfill capability ensures your analytics reports include complete trend data from day one rather than starting with only new records.

Try out all the features for free for 14 days

Related on Kondado

Try out all the features for free for 14 days