Is SoundCommerce an ETL data onboarding solution?
Fast Answer
Yes, and more. SoundCommerce onboards data to modern cloud data warehouses, providing useful data sets and data models for AI, activation and analytics.
SoundCommerce is a composable data platform with an intelligent data pipeline for advanced data onboarding and modeling; with comprehensive pre-built analytical data models for key retail functions like acquisition and retention marketing, merchandising and operations; and with natively integrated analytics and data activation.
Like ETL solutions, SoundCommerce ingests, maps and models data for hosting in modern data warehouses like Snowflake and Google Cloud BigQuery – making the data available for analysis and activation in your favorite tools and applications too. Technically speaking, SoundCommerce follows an “EtLT” pattern for data onboarding, applying data labels and definitions early, and analytical modeling late in the data life cycle.
Long Answer
Like standalone ETL solutions, SoundCommerce offers pre-built data collectors to ingest data from diverse SaaS and on-prem systems and applications. SoundCommerce onboards to modern cloud data warehouses (like Snowflake and BigQuery) your mission critical data including paid, earned and owned media campaigns; online and store POS order lifecycle and profitability; customer behavior and lifetime value; product merchandising assortment and promotions; and retail operations including store and distribution center fulfillment events through shopper doorstep delivery. SoundCommerce also provides useful, retail-ready data models that offer immediate business insights – and which are customizable to address the unique aspects of your business and business model as they evolve over time.
Unlike other ETL solutions, SoundCommerce addresses the entire data value chain. SoundCommerce offers flexible ways to collect, model, analyze and activate mission-critical data using off-the-shelf cloud infrastructure like Snowflake and Google Cloud BigQuery data warehouses (which you own and control), and modern data stack tooling like DBT, Sigma, and Census. You can use SoundCommerce interfaces to engage the analytical models, and use your favorite tools and applications too.
Following an “EtLT” pattern for data onboarding, SoundCommerce provides dedicated semantic labeling at ingest for better shared understanding of data and easier corporate data governance. SoundCommerce’s data typing, data labeling and data model logic are applied early upstream in the data pipeline, so that metrics and KPIs in analytical reports and dashboards match up with customer and audience segmentation and other data outputs and activation use cases. For retail organizations with data engineering teams, SoundCommerce provides mapping and modeling logic transparency through SoundCommerce user interfaces and by offering DBT source-code libraries for the advanced analytical logic best suited to run in the data warehouse.
SoundCommerce views ETL as just one small step of a complete, turnkey retail data infrastructure built upon the open standards of the Modern Data Stack.
SoundCommerce vs. Data Onboarding Solutions.
Similarities and Differences.
SoundCommerce as an ETL Solution
“SoundCommerce IS an ETL data onboarding solution. The composable data platform offers prebuilt adapters for data ingest and a complete transformation solution to unify and map source data to common tables in open cloud data warehouses like Snowflake and BigQuery.”
The SoundCommerce Difference
“SoundCommerce is not just an ETL data onboarding solution. Following an ‘EtLT’ pattern for data onboarding, SoundCommerce offers advanced data typing, semantic labeling and semantic mapping capabilities. Further downstream, SoundCommerce offers prebuilt analytical models to support natively integrated AI, analytics and activation tools.”
Learn how SoundCommerce provides Hearst retail-ready analytical and behavioral data models, populated by an intelligent data pipeline that promotes data visibility, transparency, and trust.
It’s Time to Move on from Standalone ETL Solutions.
Cloud data warehouses like Google Cloud BigQuery and Snowflake provide the opportunity to do more with data than ever before. Modern tooling makes data work accessible to engineers and non-engineers alike, accelerating insights and actions across the organization. But cost and complexity are real concerns in the cloud data era, and the right approach and architecture can minimize your cost, time and risk to make data a true competitive advantage.
When implementing your cloud data warehouse, look for ways to simplify the data work by tackling key considerations both upstream and downstream of the data store.
- Immutably logging your data ahead of the data warehouse reduces analytical load on operational systems, and makes reinterpretation of data easier in the future.
- A robust semantic layer, defined and applied at data ingest simplifies downstream understanding and governance.
- No code/low code interfaces calling shared libraries of mappings and models improve access to data and accelerate time to value.
- Streaming data through your data pipelines unlocks real-time use cases ranging from site personalization to triggered customer communications.
In concert, all of these EtLT features make data more available and useful across the entire enterprise – driving competitive advantage and profitable growth.