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Cognizant
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Deleting duplicate rows in SQL
Use the DISTINCT keyword in SELECT statement to retrieve unique rows
Use GROUP BY clause to group rows with same values and then use aggregate functions to select one row
Use the ROW_NUMBER() function to assign a unique number to each row and then delete the rows with duplicate numbers
To remove header and trailer from a sequential data file in Datastage.
Use Sequential File stage in Datastage.
Set the 'Skip Rows' property to the number of header rows to be skipped.
Set the 'Trailer Rows' property to the number of trailer rows to be skipped.
Use a Transformer stage to remove any remaining header or trailer rows.
Use the 'Remove' function in the Transformer stage to remove the rows.
To kill a job in Datastage
Stop the job manually from the Director client
Terminate the job from the command line using the dsjob command
Kill the job process from the operating system level
Delete the job from the Datastage repository
To find process id in Linux, use the command 'ps -aux | grep
Open the terminal
Type 'ps -aux' to list all running processes
Use 'grep
The process id (PID) will be listed in the second column
SQL queries with window functions
Window functions perform calculations across a set of rows that are related to the current row
Common window functions include ROW_NUMBER, RANK, DENSE_RANK, and NTILE
Window functions are used with the OVER() clause to define the window or subset of rows to perform the calculation on
SORT BY, ORDER BY, CLUSTER BY, and DISTRIBUTE BY are SQL clauses used for data sorting and partitioning.
SORT BY is used to sort the result set in ascending or descending order based on one or more columns.
ORDER BY is used to sort the result set in ascending or descending order based on one or more columns. It is similar to SORT BY but can be used with other clauses like LIMIT and OFFSET.
CLUSTER BY is used to group data...
Small file problem refers to the issue of having a large number of small files in a storage system.
Small files can cause inefficiencies in storage and processing.
Solutions include consolidating small files into larger ones or using a different storage system.
Examples include Hadoop's SequenceFile format and Amazon S3's object size optimization.
RDS, VA, DF, VS, and DS are all acronyms related to data engineering.
RDS stands for Relational Database Service, a managed database service by AWS.
VA stands for Virtual Assistant, a software program that can assist with tasks.
DF stands for Dataflow, a managed service by Google Cloud for data processing.
VS stands for Virtual Server, a server that runs on a virtual machine.
DS stands for Datastore, a NoSQL document databa
I applied via Recruitment Consultant and was interviewed in Sep 2021. There were 3 interview rounds.
What people are saying about Cognizant
I applied via Recruitment Consultant and was interviewed before Jun 2020. There were 4 interview rounds.
Reading data from a .log file and extracting columns with a specific regex.
Use Python's built-in 're' module to define the regex pattern.
Open the .log file using Python's 'open' function.
Iterate through each line of the file and extract the desired columns using the regex pattern.
Store the extracted data in a data structure such as a list or dictionary.
Optimizations for data engineering
Use indexing to speed up queries
Partition data to improve query performance
Use caching to reduce data retrieval time
Optimize data storage format for faster processing
Use parallel processing to speed up data processing
Optimize network bandwidth usage
Use compression to reduce storage and network usage
Answering how to read JSON in Python.
Use the json module to load and parse JSON data
Use the json.loads() method to load JSON data from a string
Use the json.load() method to load JSON data from a file
Access JSON data using keys or indexes
Use the json.dumps() method to convert Python objects to JSON strings
Pyspark configs
Pyspark configs are used to configure the behavior of a Pyspark application.
They can be set using SparkConf object or spark-submit command.
Examples include setting the number of executors, memory allocation, and log level.
Cognizant interview questions for designations
I applied via campus placement at RC Patel College of Education, Shirpur and was interviewed in Oct 2024. There were 3 interview rounds.
There are some general aptitude questions.
There were two simple codes from which we need to pass the test case for at least one code
The most difficult subject in college was Advanced Calculus.
Advanced Calculus involved complex mathematical concepts and required a deep understanding of calculus principles.
The subject required a lot of practice and problem-solving skills to master the concepts.
Topics such as multivariable calculus, differential equations, and vector calculus were particularly challenging.
The abstract nature of the subject made it dif...
I am a recent graduate with a degree in Computer Science and a passion for data engineering.
Graduated with a degree in Computer Science
Strong interest in data engineering
Completed internships in data analysis and database management
I applied via Recruitment Consultant and was interviewed in Feb 2021. There were 4 interview rounds.
To recommend customers to migrate to the cloud, assess their current infrastructure, plan the migration strategy, choose the right cloud provider, and ensure data security.
Assess the customer's current infrastructure and identify the applications and data that can be migrated to the cloud.
Plan the migration strategy by considering factors like cost, time, and resource requirements.
Choose the right cloud provider based ...
I applied via Walk-in and was interviewed in Dec 2024. There were 5 interview rounds.
Given task Statics standard deviations Attrition Average of given table values and Given graph economi graph and poverty graph base on that need to gave answers 30 qustion and 60 min time duration
I applied via Naukri.com and was interviewed in Nov 2024. There was 1 interview round.
I am a Senior Data Engineer with experience in building scalable data pipelines and optimizing data processing workflows.
Experience in designing and implementing ETL processes using tools like Apache Spark and Airflow
Proficient in working with large datasets and optimizing query performance
Strong background in data modeling and database design
Worked on projects involving real-time data processing and streaming analytic
Decorators in Python are functions that modify the behavior of other functions or methods.
Decorators are defined using the @decorator_name syntax before a function definition.
They can be used to add functionality to existing functions without modifying their code.
Decorators can be used for logging, timing, authentication, and more.
Example: @staticmethod decorator in Python is used to define a static method in a class.
SQL query to group by employee ID and combine first name and last name with a space
Use the GROUP BY clause to group by employee ID
Use the CONCAT function to combine first name and last name with a space
Select employee ID, CONCAT(first_name, ' ', last_name) AS full_name
Constructors in Python are special methods used for initializing objects. They are called automatically when a new instance of a class is created.
Constructors are defined using the __init__() method in a class.
They are used to initialize instance variables of a class.
Example: class Person: def __init__(self, name, age): self.name = name self.age = age person1 = Person('Alice', 30)
Indexing in SQL is a technique used to improve the performance of queries by creating a data structure that allows for faster retrieval of data.
Indexes are created on columns in a database table to speed up the retrieval of rows that match a certain condition in a WHERE clause.
Indexes can be created using CREATE INDEX statement in SQL.
Types of indexes include clustered indexes, non-clustered indexes, unique indexes, an...
Spark works well with Parquet files due to its columnar storage format, efficient compression, and ability to push down filters.
Parquet files are columnar storage format, which aligns well with Spark's processing model of working on columns rather than rows.
Parquet files support efficient compression, reducing storage space and improving read performance in Spark.
Spark can push down filters to Parquet files, allowing f...
I applied via Recruitment Consulltant and was interviewed in Nov 2024. There were 2 interview rounds.
Different types of joins available in Databricks include inner join, outer join, left join, right join, and cross join.
Inner join: Returns only the rows that have matching values in both tables.
Outer join: Returns all rows when there is a match in either table.
Left join: Returns all rows from the left table and the matched rows from the right table.
Right join: Returns all rows from the right table and the matched rows ...
Implementing fault tolerance in a data pipeline involves redundancy, monitoring, and error handling.
Use redundant components to ensure continuous data flow
Implement monitoring tools to detect failures and bottlenecks
Set up automated alerts for immediate response to issues
Design error handling mechanisms to gracefully handle failures
Use checkpoints and retries to ensure data integrity
AutoLoader is a feature in data engineering that automatically loads data from various sources into a data warehouse or database.
Automates the process of loading data from different sources
Reduces manual effort and human error
Can be scheduled to run at specific intervals
Examples: Apache Nifi, AWS Glue
To connect to different services in Azure, you can use Azure SDKs, REST APIs, Azure Portal, Azure CLI, and Azure PowerShell.
Use Azure SDKs for programming languages like Python, Java, C#, etc.
Utilize REST APIs to interact with Azure services programmatically.
Access and manage services through the Azure Portal.
Leverage Azure CLI for command-line interface interactions.
Automate tasks using Azure PowerShell scripts.
Linked Services are connections to external data sources or destinations in Azure Data Factory.
Linked Services define the connection information needed to connect to external data sources or destinations.
They can be used in Data Factory pipelines to read from or write to external systems.
Examples of Linked Services include Azure Blob Storage, Azure SQL Database, and Amazon S3.
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