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Happiest Minds Technologies
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I have experience working on projects involving data pipeline development, ETL processes, and data warehousing.
Developed ETL processes to extract, transform, and load data from various sources into a data warehouse
Built data pipelines to automate the flow of data between systems and ensure data quality and consistency
Optimized database performance and implemented data modeling best practices
Worked on real-time data pro...
I applied via Recruitment Consulltant and was interviewed in Apr 2023. There were 4 interview rounds.
What people are saying about Happiest Minds Technologies
I applied via Naukri.com and was interviewed in Oct 2024. There were 2 interview rounds.
Spark performance problems can arise due to inefficient code, data skew, resource constraints, and improper configuration.
Inefficient code can lead to slow performance, such as using collect() on large datasets.
Data skew can cause uneven distribution of data across partitions, impacting processing time.
Resource constraints like insufficient memory or CPU can result in slow Spark jobs.
Improper configuration settings, su...
posted on 16 Nov 2024
I applied via Referral and was interviewed in Nov 2024. There was 1 interview round.
My strengths include strong analytical skills, attention to detail, and problem-solving abilities.
Strong analytical skills - able to analyze complex data sets and derive meaningful insights
Attention to detail - meticulous in ensuring data accuracy and quality
Problem-solving abilities - adept at identifying and resolving data-related issues
Experience with data manipulation tools like SQL, Python, and Spark
Seeking new challenges and growth opportunities in a different environment.
Looking for new challenges to enhance my skills and knowledge
Seeking growth opportunities that align with my career goals
Interested in exploring different technologies and industries
Want to work in a more collaborative team environment
Seeking better work-life balance or location proximity
I applied via Referral and was interviewed in Feb 2024. There was 1 interview round.
Just focus on the basics of pyspark.
posted on 9 May 2022
I applied via Approached by Company and was interviewed in Nov 2021. There was 1 interview round.
Normalization is a process of organizing data in a database to reduce redundancy and improve data integrity.
Normalization involves breaking down a table into smaller tables and defining relationships between them.
It helps in reducing data redundancy and inconsistencies.
Views are virtual tables that are created based on the result of a query. They can be used to simplify complex queries.
Stored procedures are precompiled...
I applied via Naukri.com and was interviewed in Sep 2024. There was 1 interview round.
To create a pipeline in ADF, you can use the Azure Data Factory UI or code-based approach.
Use Azure Data Factory UI to visually create and manage pipelines
Use code-based approach with JSON to define pipelines and activities
Add activities such as data movement, data transformation, and data processing to the pipeline
Set up triggers and schedules for the pipeline to run automatically
Activities in pipelines include data extraction, transformation, loading, and monitoring.
Data extraction: Retrieving data from various sources such as databases, APIs, and files.
Data transformation: Cleaning, filtering, and structuring data for analysis.
Data loading: Loading processed data into a data warehouse or database.
Monitoring: Tracking the performance and health of the pipeline to ensure data quality and reliab
getmetadata is used to retrieve metadata information about a dataset or data source.
getmetadata can provide information about the structure, format, and properties of the data.
It can be used to understand the data schema, column names, data types, and any constraints or relationships.
This information is helpful for data engineers to properly process, transform, and analyze the data.
For example, getmetadata can be used ...
Triggers in databases are special stored procedures that are automatically executed when certain events occur.
Types of triggers include: DML triggers (for INSERT, UPDATE, DELETE operations), DDL triggers (for CREATE, ALTER, DROP operations), and logon triggers.
Triggers can be classified as row-level triggers (executed once for each row affected by the triggering event) or statement-level triggers (executed once for eac...
Normal cluster is used for interactive workloads while job cluster is used for batch processing in Databricks.
Normal cluster is used for ad-hoc queries and exploratory data analysis.
Job cluster is used for running scheduled jobs and batch processing tasks.
Normal cluster is terminated after a period of inactivity, while job cluster is terminated after the job completes.
Normal cluster is more cost-effective for short-liv...
Slowly changing dimensions refer to data warehouse dimensions that change slowly over time.
SCDs are used to track historical changes in data over time.
There are three types of SCDs - Type 1, Type 2, and Type 3.
Type 1 SCDs overwrite old data with new data, Type 2 creates new records for changes, and Type 3 maintains both old and new data in separate columns.
Example: A customer's address changing would be a Type 2 SCD.
Ex...
Use Python's 'with' statement to ensure proper resource management and exception handling.
Use 'with' statement to automatically close files after use
Helps in managing resources like database connections
Ensures proper cleanup even in case of exceptions
List is mutable, tuple is immutable in Python.
List can be modified after creation, tuple cannot be modified.
List uses square brackets [], tuple uses parentheses ().
Lists are used for collections of items that may need to be changed, tuples are used for fixed collections of items.
Example: list_example = [1, 2, 3], tuple_example = (4, 5, 6)
Datalake 1 and Datalake 2 are both storage systems for big data, but they may differ in terms of architecture, scalability, and use cases.
Datalake 1 may use a Hadoop-based architecture while Datalake 2 may use a cloud-based architecture like AWS S3 or Azure Data Lake Storage.
Datalake 1 may be more suitable for on-premise data storage and processing, while Datalake 2 may offer better scalability and flexibility for clou...
To read a file in Databricks, you can use the Databricks File System (DBFS) or Spark APIs.
Use dbutils.fs.ls('dbfs:/path/to/file') to list files in DBFS
Use spark.read.format('csv').load('dbfs:/path/to/file') to read a CSV file
Use spark.read.format('parquet').load('dbfs:/path/to/file') to read a Parquet file
Star schema is denormalized with one central fact table surrounded by dimension tables, while snowflake schema is normalized with multiple related dimension tables.
Star schema is easier to understand and query due to denormalization.
Snowflake schema saves storage space by normalizing data.
Star schema is better for data warehousing and OLAP applications.
Snowflake schema is better for OLTP systems with complex relationsh
repartition increases partitions while coalesce decreases partitions in Spark
repartition shuffles data and can be used for increasing partitions for parallelism
coalesce reduces partitions without shuffling data, useful for reducing overhead
repartition is more expensive than coalesce as it involves data movement
example: df.repartition(10) vs df.coalesce(5)
Parquet file format is a columnar storage format used for efficient data storage and processing.
Parquet files store data in a columnar format, which allows for efficient querying and processing of specific columns without reading the entire file.
It supports complex nested data structures like arrays and maps.
Parquet files are highly compressed, reducing storage space and improving query performance.
It is commonly used ...
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