Splice Machine Training
Splice Machine is a hybrid SQL database built on Hadoop and Spark that combines transactional (OLTP) and analytical (OLAP) capabilities, making it suitable for real-time applications, machine learning, and big data analytics.
Why should you choose Nisa For Splice Machine 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 Splice Machine 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
Splice Machine 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. Introduction to Splice Machine
- Overview of Splice Machine
- What is Splice Machine?
- Core architecture and components
- Use Cases
- Real-time applications
- Machine learning
- Big data analytics
2. Installation and Setup
- System Requirements
- Hadoop and Spark prerequisites
- Deployment options (cloud, on-premises, hybrid)
- Installation Steps
- Single-node setup
- Clustered deployment
- Configuring the Database
- Tuning performance settings
3. SQL on Splice Machine
- Basic SQL Operations
- Creating tables, inserting, updating, and deleting data
- Querying data using SQL
- Advanced SQL Features
- Joins, subqueries, and aggregations
- User-defined functions (UDFs)
- Window functions and analytics
4. OLTP (Transactional) Capabilities
- ACID Transactions
- Ensuring data consistency
- Indexing and Partitioning
- Improving query performance
- Concurrency Control
- Handling multiple users and transactions
5. OLAP (Analytical) Capabilities
- Real-time Analytics
- Running analytics on live data
- Integration with Spark
- Leveraging Spark for distributed processing
- Performance Tuning
- Query optimization techniques
6. Machine Learning on Splice Machine
- Machine Learning Workflow
- Data preparation and feature engineering
- Training Models on Splice Machine
- Built-in machine learning capabilities
- Using Spark MLlib with Splice Machine
- Integration and advanced use cases
7. Administration and Maintenance
- Monitoring and Troubleshooting
- Using built-in tools for database health
- Backup and Recovery
- Ensuring data safety and consistency
- Security and User Management
- Role-based access control (RBAC)
8. Integration and Connectivity
- Connecting with BI Tools
- Tableau, Power BI, and others
- APIs and SDKs
- JDBC, ODBC, and REST API integration
- Data Sources
- Connecting to external data sources (e.g., Kafka, Hadoop, relational databases)
9. Cloud Deployment
- Setting Up Splice Machine on the Cloud
- AWS, Azure, or Google Cloud
- Managing Cloud Resources
- Scaling and optimizing for cost-efficiency
10. Advanced Topics
- Streaming Data Integration
- Real-time data ingestion with Kafka
- Graph Processing
- Leveraging Splice Machine for graph analytics
- Hybrid Deployments
- Best practices for integrating on-premises and cloud data