Snowflake is a cloud-based elastic data warehouse or Relational Database Management System (RDBMS). Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings. Snowflake supports both transformation during (ETL) or after loading (ELT). Snowflake works with a wide range of data integration tools, including Informatica, Talend, Tableau, Matillion and others.
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Why enroll Redhat Linux
Why enroll for Terraform course ?
Course Benefits
A huge library ecosystem. Python offers a vast choice of libraries for AI development, which contain base-level items that save coding time. … High readability. … The flexibility of the language. … Abundant community support. … Excellent visualization options.
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WhyTerraform ?
Multi-Cloud Support
Declarative Configuration
Resource Graph
Modularity and Reusability
State Management
Plan and Apply Workflow
Extensibility
About your Terraform Certification Course
Terraform Skills Covered
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State Management
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Terraform Modules
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Dependency Management
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Terraform CLI
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Terraform Configuration Language (HCL)
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Infrastructure as Code (IaC) Principles
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Resource Provisioning
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Terraform Providers
-
Terraform Workspaces
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Terraform Best Practices
Curriculum Designed by Experts
Snowflake Course Syllabus
Creation and uses of warehouses
- scaling of warehouses up and down,in and out
- auto resume and auto suspend.
- creation of scaling policies in warehouses
Creation of tables(All types)
- standard tables
- transient tables
- temporary tables
- external tables
Auto commit and manual commit of DML commands
Linear Regression: Solving Regression Problems
- Introduction – Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
- Assess the overall effectiveness of the model
- Validation of Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
- Interpretation of Results – Business Validation – Implementation on new data
Logistic Regression : Solving Classification Problems
- Introduction – Applications
- Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
- Building Logistic Regression Model (Binary Logistic Model)
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
- Validation of Logistic Regression Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
- Interpretation of Results – Business Validation – Implementation on new data
creation of views and secure views
Supervised Learning :- Naive Bayes
- Concept of Conditional Probability
- Bayes Theorem and Its Applications
- Naïve Bayes for classification
- Applications of Naïve Bayes in Classifications
Text Mining And Analytics
- Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
- Finding patterns in text: text mining, text as a graph
- Natural Language processing (NLP)
- Text Analytics – Sentiment Analysis using Python
- Text Analytics – Word cloud analysis using Python
- Text Analytics – Segmentation using K-Means/Hierarchical Clustering
- Text Analytics – Classification (Spam/Not spam)
- Applications of Social Media Analytics
- Metrics(Measures Actions) in social media analytics
- Examples & Actionable Insights using Social Media Analytics
- Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
- Fine tuning the models using Hyper parameters, grid search, piping etc.
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Deployment
Highly Available Web Application
Infrastructure Governance and Compliance
Container Orchestration with Kubernetes
Infrastructure Monitoring and Logging
Disaster Recovery (DR) Setup
Microservices Architecture
Serverless Architecture
Hybrid Cloud Deployment
Continuous Integration and Delivery (CI/CD) Pipelines
Get Experience Of 4+ Years
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Solution for BigData Problem
-
Open Source Technology
-
Based on open source platforms
-
Contains several tool for entire ETL data processing Framework
-
It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.
-
Solution for BigData Problem
-
Open Source Technology
-
Based on open source platforms
-
Contains several tool for entire ETL data processing Framework
-
It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.
-
Solution for BigData Problem
-
Open Source Technology
-
Based on open source platforms
-
Contains several tool for entire ETL data processing Framework
-
It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.
Redhat Linux Certification Course reviews
A small river named Duden flows by their place and supplies it with the necessary regelialia. It is a paradisematic country, in which river named Duden flows by their place and supplies it with the necessary
Redhat Linux System Administration - Roles and Responsibilities
1. Basic user account management (creating, modifying, and deleting users).
2. Password resets and account unlocks.
3. Basic file system navigation and management (creating, deleting, and modifying files and directories).
4. Basic troubleshooting of network connectivity issues.
5. Basic software installation and package management (installing and updating software packages).
6. Viewing system logs and checking for errors or warnings.
7. Running basic system health checks (CPU, memory, disk space).
8. Restarting services or daemons.
9. Monitoring system performance using basic tools (top, df, free).
10. Running basic commands to gather system information (uname, hostname, ifconfig).
1. Intermediate user account management (setting permissions, managing groups).
2. Configuring network interfaces and troubleshooting network connectivity issues.
3. Managing file system permissions and access control lists (ACLs).
4. Performing backups and restores of files and directories.
5. Installing and configuring system monitoring tools (Nagios, Zabbix).
6. Analyzing system logs for troubleshooting purposes.
7. Configuring and managing software repositories.
8. Configuring and managing system services (systemd, init.d).
9. Performing system updates and patch management.
10. Monitoring and managing system resources (CPU, memory, disk I/O).
1. Advanced user account management (LDAP integration, single sign-on).
2. Configuring and managing network services (DNS, DHCP, LDAP).
3. Configuring and managing storage solutions (RAID, LVM, NFS).
4. Implementing and managing security policies (firewall rules, SELinux).
5. Implementing and managing system backups and disaster recovery plans.
6. Configuring and managing virtualization platforms (KVM, VMware).
7. Performance tuning and optimization of system resources.
8. Implementing and managing high availability solutions (clustering, load balancing).
9. Automating system administration tasks using scripting (Bash, Python).
10. Managing system configurations using configuration management tools (Ansible, Puppet).
1. Learning basic shell scripting for automation tasks. 2. Understanding file system permissions and ownership. 3. Learning basic networking concepts (IP addressing, routing). 4. Learning how to use package management tools effectively. 5. Familiarizing with common Linux commands and utilities. 6. Understanding basic system architecture and components. 7. Learning basic troubleshooting techniques and methodologies. 8. Familiarizing with basic security principles and best practices. 9. Learning how to interpret system logs and diagnostic output. 10. Understanding the role and importance of system backups and restores.
1. Advanced scripting and automation techniques (error handling, loops).
2. Understanding advanced networking concepts (VLANs, subnetting).
3. Familiarizing with advanced storage technologies (SAN, NAS).
4. Learning advanced security concepts and techniques (encryption, PKI).
5. Understanding advanced system performance tuning techniques.
6. Learning advanced troubleshooting methodologies (root cause analysis).
7. Implementing and managing virtualization and cloud technologies.
8. Configuring and managing advanced network services (VPN, IDS/IPS).
9. Implementing and managing containerization technologies (Docker, Kubernetes).
10. Understanding enterprise-level IT governance and compliance requirements.
1. Designing and implementing complex IT infrastructure solutions. 2. Architecting and implementing highly available and scalable systems. 3. Developing and implementing disaster recovery and business continuity plans. 4. Conducting security audits and vulnerability assessments. 5. Implementing and managing advanced monitoring and alerting systems. 6. Developing custom automation solutions tailored to specific business needs. 7. Providing leadership and mentorship to junior team members. 8. Collaborating with other IT teams on cross-functional projects. 9. Evaluating new technologies and making recommendations for adoption. 10. Participating in industry conferences, workshops, and training programs.
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- Join our Brushup Session out Support until You find Job
Get Experience Of 4+ Years
- Projects
- Real Time Protection
- Assignments
-
Solution for BigData Problem
-
Open Source Technology
-
Based on open source platforms
-
Contains several tool for entire ETL data processing Framework
-
It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.
-
Solution for BigData Problem
-
Open Source Technology
-
Based on open source platforms
-
Contains several tool for entire ETL data processing Framework
-
It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.
-
Solution for BigData Problem
-
Open Source Technology
-
Based on open source platforms
-
Contains several tool for entire ETL data processing Framework
-
It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.