Introduction To Teradata FSLDM
The Teradata FSLDM provides an extensive set of ledger accounting functions in both analytical and reporting versions, with an intuitive interface. It provides the flexibility to customize data model constructs to meet your unique business needs, including the ability to perform accrual and cash-based calculations, generate variation statements, customize interfaces, execute multi-step processes, create advanced reports and more. It is an object-oriented, client/server application for accessing Teradata databases using Ada95/Ada83 and standard relational database interfaces. Ides trainings provides you with the best Teradata Financial Services Logical Data Model (FSLDM) training. We provide Online, Corporate and Classroom trainings. We provide classroom trainings in locations like Hyderabad, Mumbai, Pune, Delhi, etc. We provide Virtual Job Support as well.
Prerequisites for Teradata FSLDM Training
Should have knowledge on Entities and Attributes.
Teradata FSLDM Training Course Details
Course Name: Teradata Financial Services Logical Data Model (FSLDM)
Mode of Training: We provide Online, Corporate and Classroom trainings. We provide Virtual Job Support as well.
Duration of Course: 30 Hrs (Can be customized as per the requirement)
Do you provide materials: Yes, if you register with Ides Trainings, the Teradata FSLDM Training materials will be provided.
Course Fee: After registering with Ides Trainings, our coordinator will contact you.
Trainer Experience: 15+years of experience
Timings: According to one’s feasibility
Batch Type: We provide all types of batches like Regular, Weekends and Fasttrack
Backup Session: If the student misses the session, we also provide backup session
Teradata FSLDM Training Course Details
Overview Of Teradata FSLDM Training
Teradata Financial Services Logical Data Model (FSLDM) is a data model used to capture complex and highly relational business information. It’s a mature and well-supported SQL-92 standard compliant database dialect that provides high performance, reliability, security and concurrency with minimal development effort. Teradata FSLDM is the definitive data source for data modeling and database development. The Teradata FSLDM Data Modeler lets you create structured, logical, database applications for B2B and e-commerce with more flexibility and power than transactional systems from any other database system. Teradata FSLDM provides a high-level representation of an enterprise’s Financial Services Logical Data Model, providing accurate and consistent metadata that allows development tools to query data in a standardized manner. The FSLDM provides a simple representation of the entire data model, allowing you to understand what is available in your data warehouse and start building more useful applications using transactional processing systems instead of code generation.
What is Teradata?
Teradata Corporation is an American computer company that sells analytic data platforms, applications and related services. Its products are meant to consolidate data from different sources and make the data available for analysis. Formerly a division of NCR Corporation, Teradata was incorporated in 1979 and separated from NCR in October 2007. In 1999, the largest database in the world was introduced using Teradata with 130 Terabytes. In 2008, Teradata 13.0 was released with Active Data Warehousing. In 2012, Teradata 14.0 was introduced. In 2014, Teradata 15.0 was introduced. The latest version of Teradata was introduced in 2016.
Features of Teradata
Parallelism – MPP Architecture
Shared Nothing Architecture – Each component in Teradata works independently
Linear Scalability – Teradata systems are highly scalable. They can scale up to 2018 Nodes
Connectivity – Teradata can connect to Channel attached systems such as Mainframe or Network-attached systems
Mature Optimizer – Parallel optimizer
SQL – ANSI
Robust Utilities
Automatic Distribution
Uses of Teradata
To import/ export data from/ to Teradata system
Fast load
Multi load
Tpump
Fast export
TPT – Teradata Parallel Transporter
What is Financial Services Logical Data Model (FSLDM)?
A FSLDM captures key entities, attributes and relationships. It provides a deliverable for all data-related requirements needed to support the business process. If capturing process specific requirements in a logical data model is the key then the lock used to open the door is the integration of these processes.
Logical data model should be used as a clear and concise deliverable to manage complex systems as they continue to grow and evolve. It should be used as the ground truth to simplify the management of requirements in a central location. A logical data model needs to be the medium to capture tribal knowledge and relieve the bottleneck placed on key resources within a company, unleashing the power of teamwork and maximizing company effectiveness. It must reduce confusion on projects and prevent requirements lost in project delivery.
Entity Types
Attributes
Relationships and
Domains
Why Entity Relation/ Studio Data Architect for Teradata?
It has extremely intuitive and easy to use as a data modeling tool. It has a rule-based data model development, error handling, logical and physical data modeling and a collaborative working environment. It has interactive capabilities for code generation and reporting functions. It is a readily share, re-use, re-purpose and reverse engineer data models for improved quality and compliance.
Data Modelling Key Techniques
Enterprise Model Management
Reuse common data elements
- Universal mappings
- Advanced compare and merge
- Sub-model management
- Metadata integration
Complete Database Lifecycle Support
Find where data originated and where to be used
- Forward and reverse engineering
- Visual data lineage
- Model completion validation
Model-driven Design Environment
Document, understand and publish
- Advanced graphics and layout
- Automated and custom transformation
- Multiple presentation formals
ER/ Studio Enterprise
- RDBMS Support
- First-class (native) support for Teradata, including proprietary scale and DW capabilities
- Optimized Alter table with script support
- Temporal data types
- MLPPI – Multi – Level Partitioned Primary Indexes
- View syntax – COUNT over
- NUMBER datatypes
- Table and Column definitions compare
- First-class (native) support for Teradata, including proprietary scale and DW capabilities
What is a Data Warehouse?
A Data Warehouse is a central, enterprise-wide database that contains information extracted from Operational Data Stores (ODS).
- Based on enterprise-wide model
- Can begin small but may grow large rapidly
- Populated by extraction/ loading data from operational systems
- Responds to end-user “what is” queries
- Can store detailed as well as summary data
Data Warehouse Models
Enterprise warehouse
- An enterprise warehouse collects all of the information about subjects spanning the entire organization.
- It provides corporate-wide data integration, usually from one or more operational systems or external information providers, and is cross-functional in scope.
- It typically contains detailed data as well as summarized data, and can range in size from a few gigabytes to hundreds of gigabytes, terabytes, or beyond.
- An enterprise data warehouse may be implemented on traditional mainframes, computer super servers, or parallel architecture platforms.
- It requires extensive business modeling and may take years to design and build.
- Data mart
- A data mart contains a subset of corporate-wide data that is of value to a specific group of users.
- The scope is confined to specific selected subjects. For example, a marketing data mart may confine its subjects to customer, item, and sales.
- The data contained in data marts tend to be summarized.
- Data marts are usually implemented on low-cost departmental servers that are Unix/Linus or Windows based.
- The implementation cycle of a data mart is more likely to be measured in weeks rather than months or years. However, it may involve complex integration in the long run if its design and planning were not enterprise-wide.
Types of Data marts
- Independent data marts are sourced from data captured from one or more operational systems or external information providers, or from data generated locally within a particular department or geographic area.
- Dependent data marts are sourced directly from enterprise data warehouses.
- Virtual warehouse
- A Virtual warehouse is a set of views over operational databases.
- For efficient query processing, only some of the possible summary views may be materialized.
- A virtual warehouse is a easy to build but requires excess capacity on operational database servers.
Advantages of Data Warehouse Development
Top-down approach to Data Warehouse Development: This approach serves as a systematic solution and minimizes integration problems.
Bottom-up approach to Data Warehouse Development: This approach to the design, development and deployment of independent data marts provides flexibility, low cost and rapid return of investment.
Conclusion
Teradata Financial Services Logical Data Model (FSLDM) training gives you the depth understanding of all modules related to it. Our expert trainers can give you the complete understanding on this course. We provide you with the structured training program. We provide you with the personalized training design which is suggested by our subject matter experts. Ides Trainings provides the best Online, Corporate and Classroom trainings for individuals and corporates as well. We provide Virtual Job Support as well.