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Data warehouse ⇄ CRM

Apache Hive to Gladly integration — real-time, two-way sync

Keep Apache Hive and Gladly in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Apache Hive and Gladly

Sync Gladly into Apache Hive continuously and push warehouse results back onto CRM records, one two-way connection instead of two pipelines.

The CRM feeds the warehouse and the warehouse should feed the CRM: relationship data flows one way, and computed scores, segments, and customer context flow back. Most teams build the first half as a batch pipeline and never quite get to the second.

Stacksync does both with one connection. Conversation items, Agents, Topics, Tasks from Gladly land in Apache Hive as live tables, updated within seconds, and columns computed in Apache Hive write back to fields in Gladly. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • Mirror conversations and topics into a warehouse for CX analytics and staffing models.
  • Keep customer attributes such as loyalty tier and lifetime value synced from a warehouse into Gladly profiles for routing and prioritization.
  • Extract curated Hive tables into operational databases or SaaS tools so business teams use data locked in Hadoop.
  • Load records from CRMs and databases into partitioned Hive tables for long-term analytical storage.

CRM analytics on live data

Accounts, contacts, and activity from Gladly are queryable in Apache Hive moments after they change, so dashboards stop lagging the reality they describe.

Scores and segments back on the record

Lead scores, churn risk, or usage segments computed in Apache Hive appear as fields in Gladly, where the people working accounts actually see them.

A single customer view

Join Gladly's relationship data with billing, product, and support data in Apache Hive to build the customer picture the CRM alone cannot hold.

What you can sync between Apache Hive and Gladly

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Apache Hive objects Gladly objects
Views Logical views readable as modeled sources. Topics Categorization applied to conversations; the key dimension for contact-driver reporting.
Materialized Views Precomputed results available in newer Hive versions for faster reads. Tasks Follow-up work items created from external triggers or synced for workload reporting.
ACID Tables ORC-backed transactional tables that support row-level insert, update, and delete. Customer profiles The central entity; merges identifiers like email, phone, and order IDs, which syncs use for matching.
Metastore Catalog The schema registry other engines (Spark, Presto, Impala) also read. Conversations Each customer's continuous timeline; status and outcomes sync to CRMs and warehouses.
Databases Metastore namespaces that scope tables and grants. Conversation items Individual messages across voice, SMS, chat, and email attached to the conversation.
Managed Tables Tables whose data lifecycle Hive controls, used as warehouse destinations. Agents User records used to attribute work in CX analytics.
What ships with Apache Hive ⇄ Gladly

Connect Apache Hive and Gladly for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Hive–Gladly connection.

Real-time

Two-way sync

Changes in Apache Hive or Gladly instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Hive or Gladly data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Apache Hive or Gladly record.

Observability

Monitoring

Track your Apache Hive ⇄ Gladly sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Hive and Gladly.

How the Apache Hive and Gladly connectors work

Apache Hive

Integration surface
SQL (HiveQL) over JDBC/ODBC via HiveServer2 (Thrift)
Authentication
Deployment-dependent: Kerberos, LDAP, or username/password
Change detection
Polling on partition values or timestamp columns; no general-purpose change log for external consumers
Capabilities
read · write
Rate limits
No API quotas; query latency reflects the batch-oriented execution engine underneath

Gladly

Integration surface
REST API
Authentication
API tokens used with basic authentication tied to an agent email
Change detection
Webhook event subscriptions for conversation and customer events, supplemented by polling and report exports
Capabilities
read · write · webhooks
Rate limits
Subject to the platform's API rate limits
How it works

How to connect Apache Hive to Gladly — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Apache Hive and Gladly with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Apache Hive connected
    Gladly connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Hive and Gladly objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Apache Hive ⇄ Gladly
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Apache Hive Gladly
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Hive and Gladly integration FAQ

SECURITY

Security teams love Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

Learn more about security
SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 386 integrations available for Apache Hive and Gladly.

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