How do we implement Analytics Projects

While working for our existing customers across various domains we cover the below use cases such as

  1. Showing high-level KPIs that show progress towards our four main business objectives
  2. Providing actionable context for these high-level KPIs and the ability to drill-into the detail behind them
  3. Enabling ad-hoc querying and analysis of our complete dataset by technical and non-technical users
  4. Providing a trusted, integrated analysis-ready data platform for other internal projects

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Defining your KPI Framework

Platform Architecture

Platform Architecture

Data Transformation, Data Integration and Orchestration We transform

We transform and integrate raw data that Stitch syncs from each of our SaaS application sources into an integrated, query-orientated data warehouse dataset using the dbt cloud.

Note: We also use Fivetran/Hevo as our pipeline ETL systems, depending on client requirements.

Once we’d decided to work with a data pipeline-as-a-service such as Stitch together with a SQL-based data management platform like Google BigQuery, the decision to transform and integrate our data via a series of SQL SELECT will be the obvious next design choice; by using dbt and version-controlling our scripts in a Github repository we increased our productivity when developing these transformation, adhered to modern software design principles and avoided cut-and-paste scripting in-favour of templated, maintainable code

Watch out this page for our open source coverage of low cost analytics project implementation. We fondly call it as $0 Analytics

DBT Capabilities Data Modelling, Documentation and Orchestration

According to dbt, the tool is a development framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and fun.

We are official Consulting partner for DBT

dbt (data build tool) makes data engineering activities accessible to people with data analyst skills to transform the data in the warehouse using simple select statements, effectively creating your entire transformation process with code. You can write custom business logic using SQL, automate data quality testing, deploy the code, and deliver trusted data with data documentation side-by-side with the code. This is more important today than ever due to the shortage of data engineering professionals in the marketplace. Anyone who knows SQL can now build production-grade data pipelines, reducing the barrier to entry that previously limited staffing capabilities for legacy technologies.

In short, dbt (data build tool) turns your data analysts into engineers and allows them to own the entire analytics engineering workflow.

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