Close to Delta Lake

This page provides you with instructions on how to extract data from Close and load it into Delta Lake. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Close?

Close provides an inside sales SaaS and CRM platform that bundles calling, SMS, and email in a single platform. Users can make and receive calls and take business notes without getting on a phone or leaving the application. The software provides a single automated sales workflow system.

What is Delta Lake?

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

Getting data out of Close

You can use Close's REST API to get data about contacts, leads, opportunities, and many more objects into your data warehouse. For example, to get a lead, you could GET /lead/{id}/.

Sample Close data

Here's an example of the kind of response you might see when querying a lead.

{
    "status_id": "stat_1ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
    "status_label": "Potential",
    "tasks": [],
    "display_name": "Wayne Enterprises (Sample Lead)",
    "addresses": [],
    "name": "Wayne Enterprises (Sample Lead)",
    "contacts": [
        {
            "name": "Bruce Wayne",
            "title": "The Dark Knight",
            "date_updated": "2019-01-06T20:53:01.954000+00:00",
            "phones": [
                {
                    "phone": "+16503334444",
                    "phone_formatted": "+1 650-333-4444",
                    "type": "office"
                }
            ],
            "created_by": null,
            "id": "cont_o0kP3Nqyq0wxr5DLWIEm8mVr6ZpI0AhonKLDG0V5Qjh",
            "organization_id": "orga_bwwWG475zqWiQGur0thQshwVXo8rIYecQHDWFanqhen",
            "date_created": "2019-01-01T00:54:51.331000+00:00",
            "emails": [
                {
                    "type": "office",
                    "email_lower": "thedarkknight@close.io",
                    "email": "thedarkknight@close.io"
                }
            ],
            "updated_by": "user_04EJPREurd0b3KDozVFqXSRbt2uBjw3QfeYa7ZaGTwI"
        }
    ],
    "custom.lcf_ORxgoOQ5YH1p7lDQzFJ88b4z0j7PLLTRaG66m8bmcKv": "Website contact form",
    "date_updated": "2019-01-06T20:53:01.977000+00:00",
    "html_url": "https://app.close.io/lead/lead_IIDHIStmFcFQZZP0BRe99V1MCoXWz2PGCm6EDmR9v2O/",
    "created_by": null,
    "organization_id": "orga_bwwWG475zqWiQGur0thQshwVXo8rIYecQHDWFanqhen",
    "url": null,
    "opportunities": [
        {
            "status_id": "stat_4ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
            "status_label": "Active",
            "status_type": "active",
            "date_won": null,
            "confidence": 75,
            "user_id": "user_scOgjLAQD6aBSJYBVhIeNr6FJDp8iDTug8Mv6VqYoFn",
            "contact_id": null,
            "updated_by": null,
            "date_updated": "2019-01-01T00:54:51.337000+00:00",
            "value_period": "one_time",
            "created_by": null,
            "note": "Bruce needs new software for the Bat Cave.",
            "value": 50000,
            "value_formatted": "$500",
            "value_currency": "USD",
            "lead_name": "Wayne Enterprises (Sample Lead)",
            "organization_id": "orga_bwwWG475zqWiQGur0thQshwVXo8rIYecQHDWFanqhen",
            "date_created": "2019-01-01T00:54:51.337000+00:00",
            "user_name": "P F",
            "id": "oppo_8eB77gAdf8FMy6GsNHEy84f7uoeEWv55slvUjKQZpJt",
            "lead_id": "lead_IIDHIStmFcFQZZP0BRe99V1MCoXWz2PGCm6EDmR9v2O"
        },
        {
            "id": "oppo_klajsdflf8FMy6GsNHEy84f7uoeEWv55slvUjKQZpJt",
            "organization_id": "orga_bwwWG475zqWiQGur0thQshwVXo8rIYecQHDWFanqhen",
            "lead_id": "lead_IIDHIStmFcFQZZP0BRe99V1MCoXWz2PGCm6EDmR9v2O",
            "lead_name": "Wayne Enterprises (Sample Lead)",
            "status_id": "stat_4ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
            "status_label": "Active",
            "status_type": "active",
            "value": 5000,
            "value_period": "monthly",
            "value_formatted": "$50 monthly",
            "value_currency": "USD",
            "date_won": null,
            "confidence": 75,
            "note": "Bat Cave monthly maintenance cost",
            "user_id": "user_scOgjLAQD6aBSJYBVhIeNr6FJDp8iDTug8Mv6VqYoFn",
            "user_name": "P F",
            "contact_id": null,
            "created_by": null,
            "updated_by": null,
            "date_created": "2019-01-01T00:54:51.337000+00:00",
            "date_updated": "2019-01-01T00:54:51.337000+00:00"
        }
    ],
    "updated_by": "user_04EJPREurd0b3KDozVFqXSRbt2uBjw3QfeYa7ZaGTwI",
    "date_created": "2019-01-01T00:54:51.333000+00:00",
    "id": "lead_IIDHIStmFcFQZZP0BRe99V1MCoXWz2PGCm6EDmR9v2O",
    "description": ""
}

Loading data into Delta Lake on Databricks

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

Keeping Close data up to data

Now what? You've built a script that pulls data from Close and loads it into your data warehouse, but what happens tomorrow when you have new transactions?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Close's API results include fields like date_created that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Other data warehouse options

Delta Lake on Databricks is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Panoply, and To S3.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to move data from Close to Delta Lake automatically. With just a few clicks, Stitch starts extracting your Close data, structuring it in a way that's optimized for analysis, and inserting that data into your Delta Lake data warehouse.