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Snowflake Advanced Analytics Training

Live Online & Classroom Enterprise Training

Snowflake Advanced focuses on designing, optimizing, and managing enterprise-scale Snowflake solutions. It equips learners with advanced skills in performance tuning, security, governance, and complex workload management on the Snowflake Data Cloud.

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What is Snowflake Advanced Analytics Training about?

This two‐day, role‐specific course introduces participants to the advanced analytic capabilities provided by the Snowflake AI Data Cloud. Participantslearntoemploytheseanalyticalcapabilitiestoderivedeeper insights, discover patterns, and pinpoint trends. This course provides participants with the skills to begin accessing information that supports a more predictive approach to identifying what could happen instead of only looking at what has happened. The course consists of lectures, labs, demonstrations, and discussions.

What are the objectives of Snowflake Advanced Analytics Training ?

  • Use the advanced analytic capabilities provided in Snowflake to improve decision‐making.
  • Showquick insights into trends, correlations, and predictions using linear regression.
  • Exploit windowingfunctions toprovide efficient query processing and support comparative analysis.
  • Manipulate semi‐structured data in its raw format to expand analysis capabilities.
  • Explain how geospatial data can enhance operational efficiencies, support location‐based analysis, and augment visualizations.
  • Perform analysis using algorithms designed for “big data” to facilitate predictive analytics.
  • Employ complex query syntax in Snowflake.
  • Utilize Streamlit in Snowflake apps for analysis.
  • Summarize Snowflake’s LLM and ML capabilities.
  • Use a Snowflake Notebook to performance analysis and visualizations using SQL and select Python pack ages.

Who is Snowflake Advanced Analytics Training for?

  • Data Analysts
  • Citizen Data Scientists
  • Anyone interested in advanced analytics

What are the prerequisites for Snowflake Advanced Analytics Training?

  • Recommended completion of the “Snowflake Multi‐Factor Authentication (MFA) Essentials” freeon demand course.
  • Completion of “Snowflake Data Analyst” two‐day course or equivalent Snowflake knowledge.
  • Knowledge of SQL is required.
  • Familiarity with statistical analysis and its terminology is recommended.
  • Previous Python programming experience is beneficial.

Available Training Modes

Live Online Training

2 Days

Classroom Training

2 Days

Course Outline Expand All

Expand All

  • Loading Data
  • Running Queries
  • Visualizations
  • Descriptive Statistics
  • TimeTravel‐based Analytics
  • Working with Date, Time, and Timestamp
  • TimeSeries Analytics
  • Linear Regression
  • OverClause Review
  • RankandDense Rank
  • RowNumber
  • Lead and Lag
  • Other Functions
  • Unnesting Data Using Flatten
  • Using Other Semi‐structured Functions
  • Extracting and Checking Data Types
  • Creating Semi‐structured Elements From Structured Data
  • Unloading Semi‐structured Data
  • Loading Semi‐structured Data
  • SchemaDetection
  • Benefits of Structured vs Non‐structured Tables
  • Geospatial Overview
  • Geometry Data
  • Geography Data
  • Using Geospatial Functions
  • Processing Big Data
  • Comparing Data Sets
  • Top‐K Frequency
  • CountingDistinct Elements
  • Percentile Distributions
  • Streamlit Overview
  • Streamlit in Snowflake
  • Snowflake MLFunctions‐ Overview
  • Snowflake MLFunctions‐ Generalized Workflow
  • Snowflake MLFunctions‐ Specifications
  • Snowflake MLFunctions‐ Cost Considerations
  • Generative AI
  • Cortex LLM Functions‐ Overview
  • Cortex LLM Functions‐ Specialized Functions
  • Cortex LLM Functions‐ COMPLETE
  • Cortex LLM‐ Cost Monitoring
  • Introducing Snowflake Notebooks
  • Using Notebook Cells
  • Integrating Snowflake ML and Cortex LLM

Who is the instructor for this training?

The trainer for this Snowflake Advanced Analytics Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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