Get Started with Jet Analytics Data Integration

Jet Analytics Data Integration automates, orchestrates, and documents data solutions. To properly set up your environment, it is important to first understand how the platform works. The platform can be understood as an abstraction layer where data is ingested and business logic is defined, independently of the underlying infrastructure. This makes the business logic resilient and facilitates a "single version of truth", while achieving fluidity in the underlying components. In other words, infrastructure changes can be made quickly — which is often necessary in an ever-changing technical landscape — without lengthy migrations, rebuilding business logic, or compromising the "single version of truth".

Anatomy of a Jet Analytics Data Estate

Ingest Server
Windows-based server software installed on a centrally located physical or virtual machine. This software orchestrates the ingestion of data from various sources, separating data movement from the desktop software while ensuring data never leaves a user's environment.
Ingest Storage
A SQL database or Azure Data Lake where raw data is stored after ingestion from sources. By storing data in its raw form, it can be used for later analysis or retrieval.
Prepare Storage
A SQL Database, Synapse Dedicated SQL Pool, or Snowflake Data Warehouse where data from multiple sources is cleansed, transformed, and consolidated into a single version of truth.
Semantic model endpoints
A subset of related data combined into a single model or "mart". This model can be exported to multiple endpoints such as Power BI, Qlik, Tableau, Analysis Services, or CSV.
Jet Analytics Portal
A web portal handling the administration of your Jet Analytics Data Estate. Instances hold the storage connection details and maintain the configuration information implemented in Jet Analytics Data Integration.
Jet Analytics Data Integration
Windows-based desktop client software where each instance can be configured and implemented through a single, integrated user interface.

Jet Analytics Instances

Instances are the metadata components of your Jet Analytics Data Estate, which are configured in the Portal and:

Store the connection information to the associated target storage.
Contain the metadata for how each area of Jet Analytics is configured — for example, which tables are selected in an Ingest Data Source, and what relations and transformations exist in your Prepare Instance.
Maintain versioning, so each individual instance can be rolled back to a previous version.
Can be copied to another instance of the same type, allowing you to migrate your instance configuration to another storage type or environment.

Ingest Instance

This is where you ingest raw data from disparate sources into a centralized storage. An infrastructure choice must be made regarding Ingest storage, although this can always be easily adjusted at a later point.

Prepare Instance

This is where you prepare, cleanse, transform, and consolidate data in your Data Warehouse to create a "single version of truth". An infrastructure choice must be made regarding storage for your Prepare Instance, although this can always be easily adjusted at a later point.

Deliver Instance

Also referred to as semantic models, this is where you combine and deliver relevant data into data marts or models and publish them to various endpoints for business consumption. A Deliver instance can have multiple endpoints, but a starting endpoint or set of endpoints must be chosen.

Learn more about adding Instances and Data Sources.

Select a Reference Architecture

Jet Analytics supports various storage options, so environments can be configured in many different ways. The following are suggested reference architectures based on common scenarios.

Sandbox

Ideal for non-production use cases such as prototyping and demonstration.
Can be set up for free, using SQL Server Developer Edition on an existing Windows machine in less than 20 minutes.

Azure SQL Database

Balances cost and performance in an Azure cloud solution.
Ideal for production data solutions smaller than 1 TB.

Azure Synapse Dedicated SQL Pool

Geared towards big data solutions in Azure.
Ideal for production data solutions greater than 1 TB.
Near-infinite scalability, up to petabyte scale.
Extremely powerful, distributed analytics workloads.
Not suitable for smaller data solutions — in those cases, Azure SQL Database provides better performance.

Amazon Web Services (AWS)

Balances performance and cost using Amazon Web Services (AWS).
Uses an AWS VM for the application server and Amazon RDS SQL database for Ingest and Prepare storage.

On-Premise SQL Server

Ideal for production on-premise data solutions in SQL Server.

Tip: It is quick and easy to deploy Jet Analytics instances to a different environment architecture at any time. For example, if you build your instance on-premise, you can quickly deploy it onto Azure SQL DB or Azure Synapse with little to no downtime. You are not locked into your initial choice.

If you are having trouble deciding which option to select, please reach out to your Jet Analytics Partner or Solution Specialist to determine the best course of action for your goals.

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