Use Case: Analytics

6 min read

Getting started with NetSuite Analytics Warehouse in Celigo

Published Apr 8, 2024 Updated Feb 13, 2026
Celigo
Celigo

NetSuite Analytics Warehouse (NSAW) is a powerful, integrated data warehouse and analytics solution designed for Oracle NetSuite ERP users. It enables businesses to aggregate, analyze, and visualize data from NetSuite ERP alongside other business systems in a single, unified platform. This solution offers prebuilt data integration, management, and analytics capabilities, helping organizations identify patterns, gain insights, and make data-driven decisions to drive growth and operational efficiency.

With Celigo’s NetSuite Analytics Warehouse integrations, businesses can complement their NSAW solution with data from over 300 applications, greatly expanding the value of their real-time dashboards and providing comprehensive business intelligence.

This integration facilitates a deep dive into the data, unveiling patterns and insights critical for strategic decision-making and growth acceleration. The key advantage here lies in the seamless fusion of data across various platforms, providing a unified, real-time view that drives informed business strategies.

Empower every department: Popular use cases

Celigo centralizes data from your NetSuite ERP and other ecosystems into your NSAW, allowing you to analyze your data from multiple sources across your business landscape.

Omni-channel commerce: Enables a holistic analysis by integrating data from DTC/B2C, B2B, and wholesale channels, offering a comprehensive view of product and service companies.

Sales, CRM, and customer journey: Merges structured and unstructured customer interaction data with the order-to-cash process, enabling detailed visualization of sales performance and customer segmentation to pinpoint growth and retention opportunities.

Supply chain visibility: Aggregates data from ERP, TMS, WMS, 3PL, and other sources, offering operations and supply chain leaders unified dashboards and reporting for enhanced decision-making.

Customer 360 – improve customer outcomes: Analyzes support ticket data against order and fulfillment details to drive improvements in customer satisfaction metrics.

Operations – save labor cost: Assesses order demand against labor schedules, optimizing workforce utilization to reduce payroll expenses.

Manufacturing – single view of the business: Combines manufacturing ERP data with overall ERP insights, facilitating end-to-end performance metrics analysis.

Procurement – optimize channel cost: Evaluate supplier impact on procurement strategies, informing future purchasing decisions.

Marketing – increase sales pipeline/social commerce: Utilizes ad spend and inventory level analysis to forecast sales and optimize promotional strategies.

Improve warehouse productivity: Optimize warehouse productivity and customer service by integrating real-time dashboards and management applications with NetSuite ERP, analyzing labor costs, demand forecasts, and performance trends across various operations.

Benefits of NetSuite Analytics Warehouse and Celigo

  • Quick access via real-time dashboards.
  • Connects with 300+ applications for extensive data integration.
  • Facilitates complex data aggregation and calculations with reverse ETL for mapping back to NetSuite.
  • Enables you to extend prebuilt apps with business insights on a single platform, simplifying IT complexity and reducing costs.

How to create a connection in Celigo to NetSuite Analytics Warehouse and Oracle Autonomous Data Warehouse

NSAW uses Oracle Analytics Cloud (OAC) and stores your data in Oracle Autonomous Data Warehouse (OADW) to create BI visualizations in OAC.

Here, we’ll create connections with NetSuite Analytics Warehouse and Oracle Autonomous Data Warehouse using Celigo and Oracle Analytics Cloud. We’ll walk through obtaining Oracle SQL Developer, refreshing your credentials, and effectively handling connections. Learn to set up users, import data, and execute queries.

 

Introduction to Oracle SQL Developer

First, you’ll need Oracle SQL Developer. It’s crucial for the setup process, so head over to Google, find Oracle SQL Developer, and download the version suitable for your operating system.

Downloading the Cloud Wallet and resetting credentials

Once you’ve got Oracle SQL Developer ready, log into your NetSuite Analytics Warehouse instance.

Your next steps involve two crucial actions: downloading the cloud wallet from the “Warehouse” section and resetting your credentials, especially if you haven’t already set up admin credentials for your ADW instance.

These steps are fundamental for a secure and efficient connection setup.

Creating a connection in Oracle SQL Developer

With your cloud wallet downloaded and credentials updated, open Oracle SQL Developer to create a new connection to your Oracle ADW instance. This involves naming your connection, inputting the admin username, and the password you’ve reset or already have.

Remember to link the cloud wallet by browsing for the downloaded zip file, and selecting your service level—high, low, or medium, (though high is generally recommended for optimal connectivity).

Configuring a specialized user for Celigo

Next, we focus on creating a user specifically for use within Celigo. This user should have the necessary permissions to create and consume tables in the ADW instance without having broad administrative rights. Set up a username and password for this user directly in SQL Developer, ensuring a secure yet flexible environment for your data operations.

Setting up the connection in Celigo

Shifting gears to Celigo, navigate to the resources section, then to connections, to initiate a new connection setup. Choose Oracle Autonomous Database as the connection type, name your connection, and select Cloud Wallet as the authentication method.

Here, you’ll input the username and password for the user you previously created, attach the cloud wallet file, and choose your service name, opting for ‘high’ for the service level.

Integrating OAC with ADW

To fully leverage your data, it is crucial that OAC can also access your ADW instance. Within the OAC console, navigate to create a new connection to Oracle Autonomous Data Warehouse using the credentials and cloud wallet file established in Celigo.

This enables a seamless flow of data for analytics and visualization.

Creating data visualizations

With the connections securely in place in both Celigo and OAC, you’re now prepared to create data flows in Celigo to populate your ADW instance with valuable information. Subsequently, you can utilize OAC to extract this data, crafting datasets and visualizations to glean insights and drive informed decisions.

You’ve now equipped yourself to leverage the full potential of NetSuite Analytics Warehouse and Oracle ADW, enhancing your data management and visualization capabilities.

NetSuite Analytics Warehouse is a pivotal innovation for businesses leveraging Oracle NetSuite ERP, transforming raw data into actionable insights with ease and efficiency. Aggregating data from diverse sources enables deep analytical insights across various business functions. From enhancing customer experiences to optimizing supply chains and beyond, NetSuite Analytics Warehouse equips businesses with the tools needed for strategic decision-making and sustainable growth.

Unlock the power of NetSuite Analytics Warehouse

In this on-demand webinar, discover how to leverage NetSuite Analytics Warehouse for integrated data analysis across your enterprise.

Learn more →

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5 min read

Natural language BI: Query your database with no-code AI solutions

Published Jan 5, 2024 Updated Feb 13, 2026
Youssef Zouhairi

Solutions Consultant

Youssef Zouhairi

One of the significant hurdles enterprises face is the ability to tap into the extensive data stored in databases and data warehouses. This task often demands strong SQL skills for efficient data extraction and utilization. Despite the critical importance of data management, many organizations struggle with accessing and utilizing their data effectively.

A 2022 report by Gartner found that poor data quality costs organizations an average of $12.8 million annually, highlighting the significant challenges enterprises face in tapping into and effectively using the extensive data stored in databases and data warehouses.

However, using AI can be a game-changer, enabling human-like conversations with data. Celigo is revolutionizing the transformation of natural language into AI-powered SQL queries. This innovative approach allows users without SQL expertise to query and retrieve information from databases and data warehouses easily, simplifying data access.

Here, we highlight an instance of Celigo’s AI-enhanced flow assistant. In this example, we showcase a Slack-based chatbot that converts natural language into executable SQL queries, a technique that’s revolutionizing our approach to querying structured data, transforming the way we interact with our data – making it as straightforward as asking a question.

How Celigo Transforms Natural Language into SQL with AI

Generative AI and LLMs are revolutionizing data querying by enabling intuitive, natural language interactions with databases. These technologies interpret user queries and convert them into precise SQL commands, facilitating more accessible and efficient data access.

The recent launch of Celigo’s embedded AI capabilities marks a significant advancement, enhancing NLP-based flows within the platform and promising increased efficiency and innovation in data interactions.

With AI, users can easily manage data processes, including data extraction, transformation, and loading.

Building the Text-to-SQL Flow

Slack Integration

Imagine a scenario where posing a question in a Slack channel triggers a response from our bot, activating an event in integrator.io:

  • First, we set up a bot in Slack with specific permissions like “chat:write” and “channels:history”.
  • Our bot employs an ‘Event Listener’ to detect messages in “message.channels”, forwarding payloads to our endpoint, the Slack Listener in integrator.io.
  • During data processing in integrator.io, our bot updates the relevant Slack thread. Caution is needed to avoid triggering the ‘Event Listener’ again, which could create an infinite loop. To prevent this, we use an output filter or a pre-save page hook in our flow to filter out messages from our bot.

 

SQL Query Generation

The process begins by contextualizing the database schema for our LLM. We use SQL to gather a detailed schema representation.

Our prompt engineering protocol instructs the LLM to craft an SQL query following specific guidelines:

  • Limit query results to a maximum of 10, unless specified otherwise.
  • Ensure the query doesn’t modify the database, avoiding commands like CREATE, UPDATE, and DELETE.
  • If the question doesn’t directly relate to the database, the response is simply “I don’t know.”

Executing SQL Queries

Executing the SQL query on Celigo’s integrator.io platform, we ensure each result is serialized into JSON format and integrated into our data payload.

This method allows for accurate inferences and efficient outcome prediction (and the platform offers comprehensive error-handling capabilities). In case of an event where the output of the executed SQL query is null, our automated system can validate the results for accuracy, ensuring an efficient troubleshooting process.

We also use filters, hooks (scripts), and branching to efficiently handle exceptions. These functionalities not only ensure precision but also help enhance the overall performance and reliability of our process.

 

Example Use Case

Car Dealer Management

In this example, we leverage Text-to-SQL with Celigo to extract data from a Car Dealership database using natural language processing.

The process flow includes:

 

  • Extracting the dealership database schema using Celigo’s integrator.io.
  • Generating an SQL query response to the inquiry using AI.
  • Executing the query on the database.
  • If the result is null, we consider two scenarios: either the query didn’t find matches, or it lacked sufficient information.
  • The AI reevaluates and refines the SQL query for a potential alternate outcome.

Steps and Prompts

  1. Question: “Who purchased a Spider in the past 30 years?”
  2. Initially, Celigo extracts the schema of the dealership database.
  3. Subsequently, the AI module generates an SQL query response for the given inquiry.
  4. This query is then executed on the database.
  5. If the result appears null, two potential scenarios could be inferred:
    • The SQL query is accurate and did not yield any matches, indicating no “Spider” purchases in the past 30 years.
    • The SQL query appears syntactically correct but resulted in insufficient information in the original question.
  6. Acknowledging these scenarios, the AI module reevaluates the initial SQL query to determine whether a different approach might yield an alternate outcome.
  7. Upon rethinking the query, the AI module scripts an enhanced SQL query that is then executed again.
  8. Finally, AI generates the conclusion.

 

No Code, Just Words

Celigo’s iPaaS and generative AI significantly enhance the capabilities of SQL database users, enabling even those without technical expertise to query databases and extract vital business information rapidly.

Celigo’s low-code environment streamlines processes, integrating various business systems and technologies, including relational databases.

Learn how to create an AI-powered chatbot, combining Slack, OpenAI’s Generative AI language model, and Pinecone’s vector database.

This synergy creates a robust, AI-powered knowledge base, facilitating quick access to critical information, bolstering data-driven decision-making, and evolving business processes.

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