Systems | Development | Analytics | API | Testing

Push customer profiles, orders, products, and custom events into Ometria as they happen, and pull that same data back out into your warehouse or CRM, on schedule, with no engineering required. Ometria is a customer data and marketing platform built for retail and ecommerce brands. Marketing and CRM teams use it to unify customer profiles, track order history, and trigger lifecycle campaigns based on behavior like purchases, browsing, and loyalty status.

Write pipeline output directly into Databricks tables, with staging and load handled automatically, so lakehouse teams skip the intermediate warehouse hop and get data where it belongs, without maintaining a manual copy step. Databricks has become the default lakehouse for teams that need one place to store, process, and query data at scale, from raw event logs to curated tables used for BI and machine learning.

Sync contacts, conversations, tickets, and eight other Intercom streams into your warehouse, CRM, or AI pipeline, fully transformed, on a schedule you control, with no engineering required. Intercom is where support and product teams live for customer conversations. It's the system of record for tickets, live chat threads, help center articles, and the segments and tags teams use to organize customers by plan, behavior, or lifecycle stage.

SSIS is often marketed as "free with SQL Server," but the reality is far more complex and expensive. While SQL Server Integration Services comes bundled with your database license, the true cost of deploying and maintaining SSIS in 2026 extends well beyond what appears on your Microsoft invoice. With organizations increasingly evaluating modern data integration alternatives, understanding SSIS's actual total cost of ownership has never been more critical.

Insert, upsert, update, and delete records in Salesforce through the REST API, in throttled single-record or batched write modes, with per-record success and failure output on every run. Salesforce is the system of record for sales, service, and customer data at most B2B companies. Sales operations teams push lead and opportunity updates into it. Customer success teams sync account health scores.

Your data engineer just accidentally deleted the production customer table. Again. This scenario plays out in organizations worldwide, not because of malicious intent, but because of poorly configured access controls. With data breaches and compliance violations, managing who can access what in your Snowflake data warehouse isn't optional: it's essential.

Salesforce serves as the operational backbone for sales, marketing, and customer service teams worldwide. When that foundation contains duplicate records, incomplete fields, or outdated information, the consequences cascade throughout the entire organization. Sales teams waste hours chasing leads that already exist in another record. Marketing campaigns target contacts who left their companies months ago.

Database mapping sits at the core of every successful data integration, migration, and ETL project. It's the blueprint that determines whether your customer records, sales figures, and operational data arrive accurate and usable, or quietly broken. With organizations managing dozens of data sources in mid-market environments, getting database mapping right has become essential for maintaining data integrity across increasingly complex tech stacks.

GDPR data mapping has evolved from a one-time documentation exercise into continuous operational reality. With €7.1 billion in cumulative fines and regulators increasingly scrutinizing actual data practices versus paper compliance, organizations need tools that provide genuine visibility into where personal data lives, how it flows, and who processes it. The critical gap in most "GDPR compliance software" is that they help you document compliance without actually scanning your data infrastructure.

Data cleansing sits at the heart of every reliable data pipeline. Yet despite being essential to trusted analytics and decision-making, cleaning data remains one of the most time-consuming and undervalued steps in modern data workflows. With poor data quality creating significant operational challenges, getting data cleansing right has never been more critical.