Adobe, a stalwart in the software industry, is not new to introducing solutions that cater to the evolving needs of businesses. Their Adobe Experience Platform has been a comprehensive suite that allows businesses to glean valuable insights from customer data. One such offshoot from this platform is the Adobe Data Distiller, designed to streamline and enhance data processes, especially post-ingestion.
Adobe Experience Platform provides a comprehensive SQL engine capable of transforming your customer data in remarkable ways. Its versatility, adaptability, and robustness empower a wide array of applications, from straightforward data exploration to data enrichment, insights generation, and AI/ML integration. is a package offering that includes a subset of the functionalities from Adobe Experience Platform. With Adobe Data Distiller you can perform post-ingestion data preparation (such as cleaning, shaping, and manipulation) for real-time customer profile or analytical use cases by executing batch queries in Query Service. Your use of Adobe Data Distiller is dependent on your entitlement for Platform-based applications.
Saurabh Mahapatra has worked in the areas of computer vision, AI/ML, virtual reality, systems engineering, simulation, and big data analytics. He applies this knowledge to real-world problems in HR and marketing. He has written a comprehensive guide on Adobe Data Distiller:
https://data-distiller.all-stuff-data.com/
Thus far, the Guide includes:
Unit 1: Prerequisites
- Prereq 101: Why was Data Distiller Built?
- Prereq 102: Key Topics Overview: Architecture, MDM, Personas, Feature Store etc.
- Prereq 103: DBVisualizer SQL Editor Setup
- Prereq 104: Ingesting CSV Data into Adobe Experience Platform
- Prereq 105: Ingesting JSON Test Data into Adobe Experience Platform
Unit 2: Data Lake
- Lake 101: Exploring Ingested Batches in a Dataset
Unit 3: Real-Time Customer Data Platform
- Profile 101: Data Distillation for Movie Genre Targeting with Email Lists
- Profile 102: Data Enrichment with Derived Datasets
Unit 4: Identity Graph
- ID 101: Channel Identity Lookup Table from Profile Attribute Snapshot Data
- ID 601: Data Distiller Lambda Functions: Exploring Similarity Joins with Jaccard
- Similarity Measure
Unit 5: Segmentation
- Seg 101: Build Segment Overlaps using Profile Attribute Snapshot Datasets
Unit 6: Adobe Journey Optimizer
- AJO 101: Exploring Adobe Journey Optimizer Datasets (in Draft)
Unit 7: Insights
- Insights 102: Creating Your First Table in the Accelerated Store
- Insights 201: Exploring Behavioral Data – Case Study with Adobe Analytics Data
- Insights 202: Web Analytics
Unit 8: Data Science
- DS 101: Python & JupyterLab Setup
- DS 102: Basic Python
Adobe Data Distiller emerges as a robust tool for businesses seeking effective data management, especially post-ingestion. Its deep integration with the Adobe Experience Platform, combined with its unique features, makes it a promising addition to the Adobe suite. Businesses considering adopting this tool should weigh its offerings against their specific needs but can generally expect Adobe’s signature reliability and performance.
