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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning LifecycleIntroduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data InsightsDescriptive StatisticsInferential StatisticsHow to Understand Population Distributions?
Reading Data Files into PythonDifferent Variable Datatypes
Probability for Data ScienceBasic Concepts of ProbabilityAxioms of ProbabilityConditional Probability
Central Tendencies for Continuous VariablesSpread of DataKDE plots for Continuous VariableOverview of Distribution for Continuous variablesNormal DistributionSkewed DistributionSkeweness and KurtosisDistribution for Continuous Variable
Central Tendencies for Categorical VariablesUnderstanding Discrete DistributionsPerforming EDA on Categorical Variables
Dealing with Missing ValuesUnderstanding OutliersIdentifying Outliers in DataOutlier Detection in PythonOutliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and PopulationCentral Limit TheoremConfidence Interval and Margin of Error
Bivariate Analysis Introduction
CovariancePearson CorrelationSpearman's Correlation & Kendall's TauCorrelation versus CausationTabular and Graphical methods for Bivariate AnalysisPerforming Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical VariablesIntroduction to Hypothesis TestingP-valueTwo sample Z-testT-testT-test vs Z-testPerforming Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares TestBivariate Analysis on Categorical Categorical Variables
Multivariate AnalysisA Comprehensive Guide to Data ExplorationThe Data Science behind IPL
Supervised Learning vs Unsupervised LearningReinforcement LearningGenerative and Descriminative ModelsParametric and Non Parametric model
Machine Learning PipelinePreparing DatasetBuild a Benchmark Model: RegressionBuild a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should knowConfusion MatrixAccuracyPrecision and RecallAUC-ROCLog LossR2 and Adjusted R2
Dealing with Missing ValuesReplacing Missing ValuesImputing Missing Values in DataWorking with Categorical VariablesWorking with OutliersPreprocessing Data for Model Building
Understanding Cost FunctionUnderstanding Gradient DescentMath Behind Gradient DescentAssumptions of Linear RegressionImplement Linear Regression from ScratchTrain Linear Regression in PythonImplementing Linear Regression in RDiagnosing Residual Plots in Linear Regression ModelsGeneralized Linear ModelsIntroduction to Logistic RegressionOdds RatioImplementing Logistic Regression from ScratchIntroduction to Scikit-learn in PythonTrain Logistic Regression in pythonMulticlass using Logistic RegressionHow to use Multinomial and Ordinal Logistic Regression in R ?Challenges with Linear RegressionIntroduction to RegularisationImplementing RegularisationRidge RegressionLasso Regression
Introduction to K Nearest NeighboursDetermining the Right Value of K in KNNImplement KNN from ScratchImplement KNN in Python
Bias Variance TradeoffIntroduction to Overfitting and UnderfittingVisualizing Overfitting and UnderfittingSelecting the Right ModelWhat is Validation?Hold-Out ValidationUnderstanding K Fold Cross Validation
Introduction to Feature SelectionFeature Selection AlgorithmsMissing Value RatioLow Variance FilterHigh Correlation FilterBackward Feature EliminationForward Feature SelectionImplement Feature Selection in PythonImplement Feature Selection in R
Introduction to Decision TreePurity in Decision TreeTerminologies Related to Decision TreeHow to Select Best Split Point in Decision Tree?Chi-SquaresInformation GainReduction in VarianceOptimizing Performance of Decision TreeTrain Decision Tree using Scikit LearnPruning of Decision Trees
Introduction to Feature EngineeringFeature TransformationFeature ScalingFeature EngineeringFrequency EncodingAutomated Feature Engineering: Feature Tools
Introduction to Naive BayesConditional Probability and Bayes TheoremIntroduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!Working of Naive BayesMath behind Naive BayesTypes of Naive BayesImplementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification ProblemEvaluation Metrics: Multi Class Classification
Introduction to Ensemble TechniquesBasic Ensemble TechniquesImplementing Basic Ensemble TechniquesFinding Optimal Weights of Ensemble Learner using Neural NetworkWhy Ensemble Models Work well?
Introduction to StackingImplementing StackingVariants of StackingImplementing Variants of StackingIntroduction to BlendingBootstrap SamplingIntroduction to Random SamplingHyper-parameters of Random ForestImplementing Random ForestOut-of-Bag (OOB) Score in the Random ForestIPL Team Win Prediction Project Using Machine LearningIntroduction to BoostingGradient Boosting AlgorithmMath behind GBMImplementing GBM in pythonRegularized Greedy ForestsExtreme Gradient BoostingImplementing XGBM in pythonTuning Hyperparameters of XGBoost in PythonImplement XGBM in R/H2OAdaptive BoostingImplementing Adaptive BoosingLightGBMImplementing LightGBM in PythonCatboostImplementing Catboost in Python
Different Hyperparameter Tuning methodsImplementing Different Hyperparameter Tuning methodsGridsearchCVRandomizedsearchCVBayesian Optimization for Hyperparameter TuningHyperopt
Understanding SVM AlgorithmSVM Kernels In-depth Intuition and Practical ImplementationSVM Kernel TricksKernels and Hyperparameters in SVMImplementing SVM from Scratch in Python and R
Introduction to Principal Component AnalysisSteps to Perform Principal Compound AnalysisComputation of Covariance MatrixFinding Eigenvectors and EigenvaluesImplementing PCA in pythonVisualizing PCAA Brief Introduction to Linear Discriminant AnalysisIntroduction to Factor Analysis
Introduction to ClusteringApplications of ClusteringEvaluation Metrics for ClusteringUnderstanding K-MeansImplementation of K-Means in PythonImplementation of K-Means in RChoosing Right Value for KProfiling Market Segments using K-Means ClusteringHierarchical ClusteringImplementation of Hierarchial ClusteringDBSCANDefining Similarity between clustersBuild Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to DaskWorking with CuML
Introduction to Machine Learning InterpretabilityFramework and Interpretable Modelsmodel Agnostic Methods for InterpretabilityImplementing Interpretable ModelUnderstanding SHAPOut-of-Core MLIntroduction to Interpretable Machine Learning ModelsModel Agnostic Methods for InterpretabilityGame Theory & Shapley Values
Introduction to AutoMLImplementation of MLBoxIntroduction to PyCaretTPOTAuto-SklearnEvalML
Pickle and JoblibIntroduction to Model Deployment
Deploying Machine Learning Model using StreamlitDeploying ML Models in DockerDeploy Using StreamlitDeploy on HerokuDeploy Using NetlifyIntroduction to Amazon SagemakerSetting up Amazon SageMakerUsing SageMaker Endpoint to Generate InferenceDeploy on Microsoft Azure CloudIntroduction to Flask for ModelDeploying ML model using Flask
Model Deployment in AndroidModel Deployment in Iphone

Rethinking Data Products With Snowflake Native Apps for Next-Gen Secure Deployment

Dentsu Global Services (DGS)
Dentsu Global Services (DGS) Last Updated : 13 Oct, 2025
4 min read

What if an application could deliver personalized intelligence to customers, without ever seeing their data?

For product developers, the skill of operating in a world of rising data privacy demands is a must-have today. As organizations shift from traditional analytics to embedded intelligence, the need for highly secure and scalable applications has become mission-critical.

Snowflake Native Apps Framework is not just another product; it marks a paradigm shift in how we build and distribute data products. It challenges the status quo that every software must control the data it interacts with. Instead, it flips the model; your app not only runs next to the data, but in the customer’s environment as well to ensure transparency.

This is what the future of secure, scalable, and productized data applications looks like.

Table of contents

The New Product Paradigm: Application Logic Meets Data Boundaries

Traditionally, product teams have had to make trade-offs between security, usability, and scale. You either move the data to your app (with all the governance headaches that come with it) or you accept limited flexibility within the customer’s environment.

Snowflake Native Apps break that compromise. By separating application logic from the customer data layer, the framework empowers the providers to deliver rich, interactive capabilities while customers retain full control over their environment.

It’s a provider-consumer model:

  • Providers ship the logic, configuration, and experience.
  • Consumers install the app in their Snowflake account for full data autonomy.

For product developers, this changes everything. You are no longer managing deployments; you are creating modular, installable services that feel like Saas, behave like native apps, and respect enterprise-grade governance.

Secure Data Sharing: Unlocking the Power of Proprietary + Customer Intelligence

Of course, building a great product isn’t about logic. Increasingly, applications need to combine proprietary asset models, benchmarks, and enriched datasets with real customer data to deliver value.

That’s where the Snowflake Secure Data Sharing and Direct Share come into play. Instead of moving data, you share access. Tightly scoped, permissioned, real-time.

Imagine building an app that delivers pricing recommendations in the Insurance domain

  • Your customer’s historical data lives in their Snowflake account.
  • Your proprietary market model sits in the provider’s environment.
Snowflake Native App
Flowchart depicting the concept of Native App fundamentals. (Source: Snowflake)

The app runs the data strategy within its environment, combining both, without either party moving data. This isn’t just technical elegance; it’s a new trust model. One that enables collaboration, monetization, and innovation, without the usual infrastructure debt or compliance risks.

Scaling Up Production: From Single Implementations to Marketplace Distribution

Once your app is battle-tested, how do you scale distribution?

Enter Snowflake Listings, think of them as productized, discoverable data plus logic bundles that are shareable privately within our organization or publicly on the Snowflake Marketplace.

Combined with Native Apps, listings became a powerful commercial channel:

  • You can use your data strategy with different tenants.
  • There is no data movement, and your data is safe.
  • Your application turns from a project into a finished product.

Additionally, this establishes a clear path for teams wishing to monetize their proprietary IP: Path+ List+ Scale, all within the Snowflake ecosystem.

Beyond the Backend: Using Streamlit to Provide Genuine Experiences

The Snowflake Native App’s smooth workflow integration with the user experience is one of its most alluring features.

Streamlit allows Snowflake to run a Python-Native user interface layer natively. With dashboards, charts, toggles, and forms that are all directly connected to data and logic you have already created, it enables you to add visual interactivity to your application.

No embedded analytics tool, no external servers. Just a user-friendly, safe front-end that operates inside the client’s environment and maintains strict control over everything.

Packaging the App: Creating a Product Rather Than Just Code

A complete Snowflake Native App is an installable, self-contained product that is more than just a few lines of code.

Here’s what goes into the package:

  • Set up Scripts: Create roles, procedures, tables, and objects within the client’s Snowflake account using setup scripts.
  • Manifest File: Specifies the sequence in which setup scripts run.
  • Streamlit App Directory: Your front-end interactive logic.
  • Environment file: Any third-party dependencies that your app needs.

Bundle this into a named stage. Run the application packaging demand, and you get yourself a distributable unit; Testable, Versioned, and Upgrade-Ready.

Additionally, Snowflake offers version control and strong debug modes to support multi-user workflows, track down errors, and roll out improvements without errors.

Marketplace Ready: Operational Excellence, Compliance, and Trust

Adding your app to the Snowflake Marketplace is not just a step towards distribution but is a milestone for trust-building as well. Before your app reaches the users, Snowflake’s Native Apps Anti-Abuse Pipeline Service (NAAPS) checks for threats to ensure operational integrity. Following these rules as a developer is what distinguishes enterprise-ready products from experimental tools.

This cannot be negotiated, as this serves as a cornerstone for developing apps that scale with confidence, serve clients worldwide, and function in regulated environments.

Conclusion

Snowflake Native Apps offer an innovative method of thinking in addition to a technical framework. You are no longer stitching together SQL Logic, Pipelines, and Frontends. Instead, you build modular, governed, and secure applications that scale like software but sit natively inside customer environments.

You’re shipping:

  • Application framework
  • Proprietary intelligence
  • Streamlit interfaces
  • And confidence

All of this is encapsulated in a single logic that is activated by a single command. In a world where value moves quickly but data remains static, Snowflake Native Apps are the architecture that product teams have been longing for.

This blog was shaped with the framework provided by Reshma Murdeshwar, Associate Technical Architect at DGS, whose insights laid the foundation for this narrative.

Dentsu Global Services (DGS)
Dentsu Global Services (DGS)

Dentsu’s global capability center, Dentsu Global Services (DGS), is shaping the future as an innovation engine. DGS has 5,600+ experts that specialize in digital platforms, performance marketing, product engineering, data science, automation and AI, with media transformation at the core. DGS delivers AI-first, scalable solutions through dentsu’s network seamlessly integrating people, technology, and craft. They blend human creativity and advanced technology, building a diverse, future-focused organization that adapts quickly to client needs while ensuring reliability, collaboration and excellence in every engagement.

DGS brings together world-class talent, breakthrough technology and bold ideas to deliver impact at scale—for dentsu’s clients, its people and the world. It’s a future-focused, industry-leading workplace where talent meets opportunity. At DGS, employees can accelerate their career, collaborate with global teams and contribute to work that shapes the future. Find out more: Dentsu Global Services

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