How to Perform Multi-Step Salesforce Lookups Before Upserts Using Low-Code ETL

Teams often receive CSV donations without Salesforce IDs. They need to match rows to existing Contacts, Accounts, or Campaigns, then upsert Opportunities or Payments. This guide explains how to implement multi-step Salesforce lookups before upserts using a low-code ETL approach. It is written for data engineers, admins, and operations teams who own file-based integrations. You will learn core concepts, design patterns, and a production-ready sequence.

Get Full LinkedIn Ad Performance Data (Beyond LinkedIn's 25-Company Limit)

LinkedIn Ads only show 25 companies in their reporting. But if you’re running demand gen for B2B clients, that’s not enough. In this quick walkthrough, Jason Spooner (Founder, Jars Digital) shows how he uses Databox to get a complete view of LinkedIn ad exposure by company—so you can: See every company engaging with your ads Match ad data to pipeline and revenue Prove marketing’s impact on sales Whether you run paid media for clients or in-house, this tip helps you unlock more insight and drive better decisions.

How to coach sales reps using activity, conversion, and revenue data

Struggling to figure out why your sales reps are underperforming? Zorana, a Sales Manager at Databox, walks through how she built a dashboard that connects activity, conversion, and revenue — all in one view. See how she identifies who needs coaching (and why) Learn how she makes data-backed decisions each week Discover how Databox helps turn sales data into action No more guessing. No more gut feelings. Just clear visibility into what drives performance.

Sensitive Data in Business Analytics: On-Prem Hosting With Analytics That Doesn't Break User Flow

Executives sometimes seem to want two things at once. Fast answers inside operational tools, and strict control over sensitive data in business analytics. The problem is friction. Many security controls add prompts, delays, and blocked screens. Users work around them or develop “muscle memory” where they click a button without fully taking in the meaning of the text they see - or have not consciously seen. If you cast your mind back, does that seem familiar to you?

Data Validation in ETL - 2026 Guide

Data validation is the cornerstone of successful ETL (Extract, Transform, Load) processes, ensuring that information flowing through your data pipeline maintains its integrity and usefulness. When data moves between systems, it can become corrupted, incomplete, or inconsistent—problems that proper validation techniques can prevent.

How to Automate Data Quality for AI and Analytics with Snowflake and Anomalo

Join Anomalo’s Jonathan Karon to learn how organizations implement automated data quality natively within Snowflake to: Securely govern structured tables and unstructured documents for AI-readiness Leverage Snowflake Native Apps and Snowpark Container Services so data never leaves your environment Detect 80% of data issues automatically without manual rules Standardize quality across all data types so BI tools and AI agents can safely operate and trace decisions.