20+ Data Engineer 1 Interview Questions and Answers

Joins are used to combine rows from two or more tables based on a related column between them.

• Types of joins include inner join, outer join (left, right, full), and cross join.

• Number of columns after join depends on the type of join and the columns being selected.

• For example, an inner join will only include rows that have matching values in both tables, resulting in a combined table with columns from both tables.

• A left outer join will include all rows from the left table and ...read more

Different kinds of views that can be created include materialized views, virtual views, and dynamic views.

• Materialized views store the result set of a query physically and are updated periodically.

• Virtual views are based on SQL queries and do not store data physically.

• Dynamic views are created on the fly based on user input or system conditions.

• Other types of views include read-only views, updatable views, and recursive views.

There are three types of integration runtime: Self-hosted, Azure, and SSIS

• Self-hosted integration runtime is installed on a local machine or a virtual machine within an on-premises network

• Azure integration runtime is managed by Azure Data Factory and runs in the Azure cloud

• SSIS integration runtime is used to run SQL Server Integration Services packages in Azure Data Factory

There are two types of triggers in Azure Data Factory: Schedule-based triggers and Event-based triggers.

• Schedule-based triggers are based on a time schedule and can be set to run at specific intervals.

• Event-based triggers are triggered by events such as the completion of a pipeline run or the arrival of new data.

• Triggers can be used to automate the execution of pipelines in Azure Data Factory.

Use Pyspark to remove regex characters from column values

• Use the regexp_replace function in Pyspark to remove regex characters from column values

• Specify the regex pattern to match and the replacement string

• Apply the regexp_replace function to the desired column in the DataFrame

Window functions in SQL are used to perform calculations across a set of table rows related to the current row.

• Window functions are used to calculate cumulative sums, averages, ranks, and more.

• They operate on a set of rows related to the current row, known as the window frame.

• Examples include ROW_NUMBER(), RANK(), SUM() OVER(), and AVG() OVER().

A dictionary is a collection of key-value pairs, while a list is a collection of ordered elements.

• A dictionary is accessed by keys, while a list is accessed by index.

• Dictionaries are unordered, while lists are ordered.

• Example: dict = {'name': 'John', 'age': 30}, list = ['apple', 'banana', 'cherry']

Left join includes all records from the left table and matching records from the right table. Right join includes all records from the right table and matching records from the left table. Full join includes all records when there is a match in either left or right table.

• Left join: includes all records from the left table and matching records from the right table

• Right join: includes all records from the right table and matching records from the left table

• Full join: includes a...read more

Incremental data loading is the process of adding new data to an existing dataset without reloading all the data.

• It involves identifying new data since the last update

• Only the new data is added to the existing dataset

• Helps in reducing processing time and resource usage

• Commonly used in data warehousing and ETL processes

Int & Float data types are used to represent whole numbers and decimal numbers respectively in programming.

• Int data type is used for whole numbers without any decimal points, such as 5, -10, 1000

• Float data type is used for numbers with decimal points, such as 3.14, -0.5, 10.75

• Int data type is typically used for counting or indexing, while Float data type is used for calculations involving fractions or decimals

SQL commands are instructions used to interact with databases to perform tasks such as querying, updating, and managing data.

• SQL commands are used to perform various operations on databases, such as SELECT, INSERT, UPDATE, DELETE.

• Examples of SQL commands include SELECT * FROM table_name, INSERT INTO table_name (column1, column2) VALUES (value1, value2), UPDATE table_name SET column1 = value1 WHERE condition, DELETE FROM table_name WHERE condition.

• SQL commands can also be used...read more

Spark performance tuning methods involve optimizing resource allocation, data partitioning, and caching.

• Optimize resource allocation by adjusting memory and CPU settings in Spark configurations.

• Partition data effectively to distribute work evenly across nodes.

• Utilize caching to store intermediate results in memory for faster access.

• Use broadcast variables for small lookup tables to reduce shuffle operations.

• Monitor and analyze Spark job performance using tools like Spark UI a...read more

A data warehouse is a centralized repository that stores structured and unstructured data from various sources for analysis and reporting.

• Data warehouses are designed for query and analysis rather than transaction processing.

• They typically store historical data and are used for creating reports, dashboards, and data visualizations.

• Data warehouses often use ETL (extract, transform, load) processes to integrate data from different sources.

• Examples of data warehouse technologies...read more

Implemented data pipelines to optimize data processing and analysis.

• Developed ETL processes to extract, transform, and load data from various sources.

• Designed and implemented data models to support business intelligence and reporting.

• Optimized query performance and data storage to improve overall system efficiency.

Catalyst optimizer is a query optimization framework in Apache Spark that improves performance by generating optimized query plans.

• Catalyst optimizer is a rule-based optimization framework.

• It leverages advanced techniques like predicate pushdown, constant folding, and null propagation.

• Catalyst optimizer helps in generating efficient query plans for Spark SQL queries.

• It performs optimizations like filter pushdown, projection pruning, and join reordering.

• Example: Catalyst optim...read more

Understanding joins between tables and their syntaxes is crucial for data engineers.

• Different types of joins include inner join, left join, right join, and full outer join.

• Syntax for joining tables typically involves specifying the tables to join, the columns to join on, and any additional conditions.

• Example: SELECT * FROM table1 INNER JOIN table2 ON table1.column = table2.column

Delete removes specific rows from a table, while Truncate removes all rows from a table.

• Delete is a DML operation, while Truncate is a DDL operation.

• Delete can be rolled back, while Truncate cannot be rolled back.

• Delete operation is slower compared to Truncate operation.

• Delete operation maintains the integrity constraints, triggers, and indexes, while Truncate does not.

Hive optimization techniques improve query performance in Hive by optimizing data storage and query execution.

• Partitioning tables based on commonly used columns to reduce data scanned during queries

• Using bucketing to evenly distribute data across files for faster reads

• Using indexes to speed up query processing by creating index tables on frequently queried columns

• Optimizing query execution by using appropriate join strategies and tuning query settings

I have experience writing SQL queries for data manipulation and analysis.

• Use SELECT statement to retrieve data from a database

• Use WHERE clause to filter results based on specific conditions

• Use JOIN clause to combine data from multiple tables