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Teradata Development Classroom Training / Teradata Development Online Training:

(Basic to Extreme Advanced Level with REAL TIME Projects) Brochure Download: PDF / WORD

DEV Real Time Training Designed for Aspiring:
  • a) Teradata Developers
  • b) Teradata Leads
  • c) ETL & Reporting Developers
  • d) Teradata DBAs & Consultants
  • e) Teradata Designer / Modeler /Architects
Batch Schedules:
Schedule Mode Duration Fee Link
1-Dec-17 8.30PM IST OnLine 40 Days 12000 Register
4-Dec-17 9AM IST ClassRoom 45 Days 6000 Register
4-Dec-17 9am IST OnLine 45 Days 12000 Register
If schedule doesn’t suit you, for
  • SEPARATE ONLINE /
  • WEEK-END /
  • ONE ON ONE /
  • CUSTOMIZED TRAINING
TeraData Training : New Batch starts every 15 Days. MSBI Training : New Batch starts every 10 Days

TERADATA FREQUENTLY ASKED QUESTIONS [DWH, DM, ARCHITECTURE, SQL, INDICES, UTILITIES and PROJECT]

Datawarehouse Fundamental Questions
  • IT applications and differences between OLTP and OLAP?
  • 1) OLTP (Online Transaction Processing)
  • Purpose: Day-Day business storage
  • Process: Front-End, Back-End process
  • Points:
  • a) Data: Less data
  • b) Model: Noramlized model (more tables,smaller tables)
  • c) Joins: More joins (So retrieval it is slower)
  • d) Access Frequency: High [Milliseconds to seconds]
  • e) Volatality: Volatile [Read, Ins, update, Delete]

  • 2) OLAP(Online Analytical Processing)
  • Purpose: For analysis and mining
  • Process: Layered architecture
  • Points:
  • a) Data: More data
  • b) Model: DeNoramlized model (Less tables, complex tables)
  • c) Joins: Less joins (So retrieval it is faster)
  • d) Access Frequency: Low [Day/week/month...]
  • e) Volatality: NonVolatile [Read]

  • What is Datawarehouse [Single line] and warehouses available in the market?
  • Hige storage area and which is suitable for decision making.
  • Ex: Teradata,Oracle,Sqlserver,Sybase,Netezza, GreenPlum etc...

  • Difference between OLAP and BI?
  • BI is a process which uses OLAP approach to gather, convert and present the data.

  • Difference between Analysis and Mining?
  • Analysis talks about 'current understanding of data' and 'past analysis of data', where as mining talks about 'future prediction of data'.
  • Note: Datamining is a knowledge analysis and discovery method.

  • How many types of decision making available in IT?
  • a) Strategic-- Past analysis [OLAP data]
  • b)Tactical--Current and recent [OLTP]

  • Difference between OLTP and ODS?
  • OLTP holds realtime data where as ODS holds near real time data[daily 4 times from OLTP]
  • ODS helps in two ways >
  • a) Input to DWH
  • b) Incase enterprise database [OLTP] fails, it acts like enterprise db and continue operations.
  • Note: All top companies use ODS in the ETL flow.
  • OLTP similar terminologies?
  • a)Operational Systems
  • b)Enterprises databases
  • c)Business Processing Systems
  • d) Transactional systems

  • Can you describe an End-End typical ETL Flow?
  • OLTP->ODS->Stage Area[Files]->DWH[Stage]-->DWH[WorkArea]->DWH[Target]

  • How many life cycle does a DWH project need?
  • Three life cycles a)ETL b) Semantic c) Reporting
  • Famous DWH life cycles: Agile, iterative incremental [Block approach], V model

  • Explain Inmon Characterstics/ Principles?
  • Inmon Characterstics:
  • a) Subject oriented: Store data based on operation but not application.
  • Ex: Savings coount subject area
  • Application2 : a)Withdraw b)deposit
  • Subject Area: withdraw+deposit single area
  • b) Integrated: Integrate from diverse applications by eliminating inconsistencies and following standards.
  • c) Nonvolatile: No change of data. [Don't modify the data] Helps to maintain history.
  • d) Timevarient: Data store base on timeframes [granularity load / grain]
  • We can perform the below operations easily.
  • a) Current understanding
  • b)Past analysis
  • c)Future predictions

  • What is granularity and which grain your project has?
  • Granularity talks about the level of data maintaining in the project. Dwh has multiple tables with multiple grains.
  • Always the lowest grain has lot of burden but improves analytical performance.
  • Ex: Day/ week/month etc.. grains Day grain is recommended for better analysis

  • What is datamart and how many types available?
  • DataMart--Data sub store for specify business/ operation / functioality May or not not be a subset of DWH.
  • Three types of datamarts
  • a)Dependent-- DWH created first and then datamart [subset of DWH]
  • Ex: ICICI DWH, Savings account dependent datamart
  • b) Independent-- Directly created from source systems [we don't use DWH here]
  • Ex: ICICI employee payroll System [No history, not much analysis required]
  • c) Logical Datamart--It is replica of another data mart

  • How do we create datamarts practically?
  • a) By using Complex Views [Materialized views] --Dependent DM
  • b) By using a complex table --Depedent DM
  • c) By using a seperate physical storage area --Independent DM

  • How many types of DWH approaches available, which approach your company following?
  • Two approaches
  • a) KIMBal approach--Datamarts-> DWH [Bottom up approach]
  • b) Inmon approach-- DWH -->Datamarts [Top down approach]
  • My company using KImbal approach for DWH implementation because of dynamic decisions and adhod requests.

  • How many types OLAP available, which is effective?
  • OLAP--Online Analytical Processing
  • It creates aggregates(sum,avg, max,min stddev,covarience etc...) for decision making.
  • ROLAP: Relational OLAP--Data and aggrgates in the relation are [OLTP area]
  • Latency time less [fresh data anlysis]
  • Slower analysis
  • Minimal set up
  • Ex: Smal, Medium
  • MLOAP: Multidimensional OLAP-- Data and aggregates in mutidimensional area[ Cube]
  • Adv: More and detailed analysis this is helpful
  • Latency time is high [ fresh data analysis is not possible]
  • Complex set up
  • Ex: Corporates
  • HOLAP:Data in relational area and aggreagates in multidimensional area [cube]
  • Ex: Latency time avg
  • Avg set
  • Desktop OLAP: Here the data and aggregates on PC based desk top applications
  • Ex: Excel, LOtus, FoxPro,VISICALC etc...
  • Explain Time hierarchy?
  • Hour-->Day-->Week-->Fort Night-->Month-->Quarter-->half Year(Semister)-->Year

  • You have two databases with same size, then dow do you identify which is OLTP or OLAP?
  • Normalized model is OLTP, Denormalized model is OLAP

  • Need of Data warehouse?
  • 1)To Analysis of data and history maintenance.
  • 2)Companies require Strategic information to face the competition in market.
  • 3)The Operation system are not designed for strategic information.
  • 4)To maintain history of data for whole Organization and to have a single place where the entire data stored.

  • What is data warehousing and Explain Approaches?
  • Many companies follow either characteristic defined by W.H.Inmon or Sean kelly.
  • a)Inmon definition
  • Subjected Oriented, Integrated, Non Volatile,Time Variant
  • b)Sean Kelly definition
  • Seperate, Available, Integrated,TimeStamped,Suject Oriented, Non Volatile, Accessible
  • c)Dwh Approaches
  • There are two Approches
  • 1.Top Down by Inmon
  • 2.Bottom Up by Ralph kimbal
  • Inmon approach:Enterprise datawarehouse structured first and next Datamart created.(TopDown).
  • Ralph kimbal:Datamart designed first, later Datamarts to Datawarehouse designed.(BottomUp).

  • What are the responsibilities of a data warehouse consultant/professional?
  • The basic responsibility of a data warehouse consultant is to ‘publish the right data’. Some of the other responsibilities of a data warehouse consultant are:
  • 1. Understand the end users by their business area, job responsibilities, and computer tolerance.
  • 2. Find out the decisions the end users want to make with the help of the data warehouse.
  • 3. Identify the ‘best’ users who will make effective decisions using the data warehouse
  • 4. Find the potential new users and make them aware of the data warehouse.
  • 5. Determining the grain of the data.
  • 6. Make the end user screens and applications much simpler and more template driven.

  • What are fundamental stages of Data Warehousing?
  • a)Offline Operational Databases - Data warehouses in this initial stage are developed by simply copying the database of an operational system to an off-line server where the processing load of reporting does not impact on the operational system's performance.
  • b)Offline Data Warehouse - Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented data structure.
  • c)Real Time Data Warehouse - Data warehouses at this stage are updated on a transaction or event basis, every time an operational system performs a transaction (e.g an order or a delivery or a booking etc.)
  • d)Integrated Data Warehouse - Data warehouses at this stage are used to generate activity or transactions that are passed back into the operational systems for use in the daily activity of the organization.

  • What is Datamart Explain Types?
  • It is a specific Subject area or Functionality or Task. It is Designed to facilitate end user Analysis.
  • Wrong Answer-- It is a subset of warehouse
  • Types of Datamarts
  • Dependent, Independent, Logical.
  • Dependent: Created from Datawarehouse as a seperate physical store.
  • Independent :Created directly from operational systems to a seperate physical store.
  • Logical: Exists as a subset of existing Datawarehouse.