
Exusia

10+ Exusia Interview Questions and Answers
Q1. Why not to use lookup for join, where we can not use join. Disadvantage of using sort component.
Using lookup for join can be inefficient and lead to performance issues. Sort component can be slow and resource-intensive.
Lookup for join can be inefficient for large datasets as it requires scanning the entire dataset for each lookup value.
Join is more optimized for joining datasets based on a common key, while lookup is better suited for smaller reference tables.
Sort component can be slow and resource-intensive, especially for large datasets. It can impact performance and ...read more
Q2. Delete files with 0KB size from a directory. (Unix)
Use find command to locate files with 0KB size and delete them using the rm command.
Use find command with -size 0 option to locate files with 0KB size in a directory
Pipe the output of find command to rm command to delete the files
Example: find /path/to/directory -type f -size 0 -exec rm {} \;
Q3. Full working of different partition components, multifiles, checkpoints and phases.
Partition components, multifiles, checkpoints, and phases in data processing.
Partition components refer to dividing data into smaller chunks for processing efficiency.
Multifiles are multiple files used to store data during processing.
Checkpoints are markers set during processing to save progress and enable restart from a specific point.
Phases represent different stages of data processing workflow.
Example: In a MapReduce job, partition components are created by the Map phase, ...read more
Q4. Delete 10 days older files. (unix)
Use find command with -mtime option to delete files older than 10 days.
Use find command with -mtime option to search for files older than 10 days.
Combine find command with -delete option to delete the files found.
Q5. Difference between star and snowflake schema
Star schema has a single centralized fact table with denormalized dimensions, while snowflake schema has normalized dimensions with multiple related tables.
Star schema is easier to understand and query due to denormalized structure.
Snowflake schema saves storage space by normalizing dimensions into separate tables.
Star schema is more suitable for data warehousing and reporting purposes.
Snowflake schema is more normalized and better for complex data models with many-to-many re...read more
Q6. Difference between OLAP, OLTP systems
OLAP is for analyzing data, OLTP is for transaction processing.
OLAP stands for Online Analytical Processing, used for complex queries and data analysis.
OLTP stands for Online Transaction Processing, used for routine transactions like insert, update, delete.
OLAP systems are designed for read-heavy workloads, while OLTP systems are designed for write-heavy workloads.
OLAP systems typically have a denormalized schema for faster query performance, while OLTP systems have a normali...read more
Q7. Abinito components usage
Abinito components are used in ETL processes for data integration and transformation.
Abinito components are used in ETL (Extract, Transform, Load) processes for data integration and transformation.
Some commonly used Abinito components include Input Table, Output Table, Join, Partition, Sort, Filter, and Lookup.
Abinito provides a graphical interface for designing ETL processes using these components.
Abinito components can be connected in a flow to define the data transformatio...read more
Q8. what is data modeling and it's importance
Data modeling is the process of creating a visual representation of data structures and relationships to help understand and analyze data.
Data modeling helps in organizing and structuring data in a way that is easy to understand and analyze.
It helps in identifying relationships between different data elements.
Data modeling is important for designing databases, developing data warehouses, and improving data quality.
Examples of data modeling tools include ER diagrams, UML diagr...read more
Q9. what is Data Profiling
Data profiling is the process of analyzing and summarizing data to gain insights into its quality, structure, and content.
Data profiling involves examining data to understand its characteristics, such as data types, patterns, and relationships.
It helps in identifying data quality issues, such as missing values, outliers, and inconsistencies.
Data profiling can also reveal data distribution, frequency of values, and data dependencies.
Examples of data profiling tools include Tal...read more
Q10. Working in current project Hiw can it be optimized
The current project can be optimized by improving code efficiency and reducing redundancy.
Identify and remove any unnecessary code or functions
Use built-in Python functions and libraries to reduce code complexity
Implement caching or memoization to reduce computation time
Optimize database queries and indexing for faster data retrieval
Use profiling tools to identify and fix performance bottlenecks
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