AtScale Training
AtScale is a data virtualization and analytics platform designed to simplify and enhance business intelligence (BI) and data management processes. It bridges the gap between enterprise-scale data warehouses, data lakes, and BI tools, allowing users to gain insights from their data without moving or transforming it into proprietary formats.
Why should you choose Nisa For AtScale Training?
Nisa Trainings is the best online training platform for conducting one-on-one interactive live sessions with a 1:1 student-teacher ratio. You can gain hands-on experience by working on near-real-time projects under the guidance of our experienced faculty. We support you even after the completion of the course and happy to clarify your doubts anytime. Our teaching style at Nisa Trainings is entirely hands-on. You’ll have access to our desktop screen and will be actively conducting hands-on labs on your desktop.
Job Assistance
If you face any problem while working on AtScale Course, then Nisa Trainings is simply a Call/Text/Email away to assist you. We offer Online Job Support for professionals to assist them and to solve their problems in real-time.
The Process we follow for our Online Job Support Service:
- We receive your inquiry for Online Job
- We will arrange a telephone call with our consultant to grasp your complete requirement and the tools you’re
- If our consultant is 100% confident in taking up your requirement and when you are also comfortable with our consultant, we will only agree to provide service. And then you have to make the payment to get the service from
- We will fix the timing for Online Job Support as mutually agreed by you and our consultant.
Course Information
AtScale Training
Duration: 25 Hours
Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
Training Method: Instructor Led Online One-on-One Live Interactive
Sessions.
COURSE CONTENT :
1. AtScale Essentials
- Introduction to AtScale
- What is AtScale?
- Key components of the platform
- Building Your First Cube
- Understanding cube design
- Data source integration
- Measures, dimensions, and hierarchies
- Navigating the AtScale User Interface
- Cube designer
- Query monitor and logs
- Practical Exercise: Creating a ClickStream Cube
2. Administration
- Installation and Setup
- System requirements and deployment
- Single-node vs. clustered environment
- Server Management
- Backups and recovery
- System monitoring and troubleshooting
- User and Role Management
- LDAP integration
- Assigning permissions and roles
- Maintenance and Upgrades
- Best practices for updating AtScale
- Troubleshooting common issues
3. Advanced Cube Design
- Advanced Features
- Aggregations and partitions
- Calculated measures and attributes
- Optimizing Cube Performance
- Performance tuning strategies
- Managing query loads
- Working with Complex Data
- Multi-fact cubes
- Handling semi-structured data
- Practical Exercise: Designing an Advanced Cube
4. Security
- Configuring Security Policies
- Project-level and cube-level security
- Role-based access control (RBAC)
- Data Masking and Filtering
- Applying data-level security
- Integration with External Authentication
- LDAP and SSO setup
- Best Practices for Data Security in AtScale
5. Time-Relative Calculated Measures
- Introduction to Time-Relative Analysis
- Use cases and applications
- Implementing Calculations
- Year-over-year and month-over-month comparisons
- Rolling averages and cumulative totals
- Debugging and Testing
- Validating time-relative calculations
6. Scaling AtScale
- Transitioning from Single-Node to Clustered Environments
- Benefits and challenges
- Step-by-step guide to clustering
- Managing Distributed Architectures
- Load balancing and fault tolerance
- Troubleshooting Performance Bottlenecks
7. Integration with BI Tools
- Connecting AtScale to BI Tools
- Tableau, Power BI, Excel, and Looker
- Semantic Layer Integration
- Maintaining consistency across BI tools
- Query Optimization
- Reducing query complexity
- Leveraging pre-aggregations and caching
8. Data Warehousing and Cloud Integration
- Connecting to Cloud Data Warehouses
- Google BigQuery, Snowflake, AWS Redshift, and more
- Managing Hybrid Data Architectures
- On-premises and cloud integration
- Best Practices for Data Virtualization
- Minimizing data movement and maximizing efficiency