Diggibyte Technologies Data Engineer Interview Questions and Answers

Optimizing SQL queries involves using indexes, avoiding unnecessary joins, and optimizing the query structure.

• Use indexes on columns frequently used in WHERE clauses

• Avoid using SELECT * and only retrieve necessary columns

• Optimize joins by using INNER JOIN instead of OUTER JOIN when possible

• Use EXPLAIN to analyze query performance and make necessary adjustments

Performance optimization in Spark involves tuning configurations, optimizing code, and utilizing caching.

• Tune Spark configurations such as executor memory, number of executors, and shuffle partitions.

• Optimize code by reducing unnecessary shuffles, using efficient transformations, and avoiding unnecessary data movements.

• Utilize caching to store intermediate results in memory and avoid recomputation.

• Example: In my projec...read more

SparkContext is the main entry point for Spark functionality, while SparkSession is the entry point for Spark SQL.

• SparkContext is the entry point for low-level API functionality in Spark.

• SparkSession is the entry point for Spark SQL functionality.

• SparkContext is used to create RDDs (Resilient Distributed Datasets) in Spark.

• SparkSession provides a unified entry point for reading data from various sources and performing

When a spark job is submitted, various steps are executed at the backend to process the job.

• The job is submitted to the Spark driver program.

• The driver program communicates with the cluster manager to request resources.

• The cluster manager allocates resources (CPU, memory) to the job.

• The driver program creates DAG (Directed Acyclic Graph) of the job stages and tasks.

• Tasks are then scheduled and executed on worker nodes ...read more

Calculate second highest salary using SQL and pyspark

• Use SQL query with ORDER BY and LIMIT to get the second highest salary

• In pyspark, use orderBy() and take() functions to achieve the same result

The two types of modes for Spark architecture are standalone mode and cluster mode.

• Standalone mode: Spark runs on a single machine with a single JVM and is suitable for development and testing.

• Cluster mode: Spark runs on a cluster of machines managed by a cluster manager like YARN or Mesos for production workloads.

Client mode is better for very less latency due to direct communication with the cluster.

• Client mode allows direct communication with the cluster, reducing latency.

• Standalone mode requires an additional layer of communication, increasing latency.

• Client mode is preferred for real-time applications where low latency is crucial.

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 ...read more

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.

ADF questions refer to Azure Data Factory questions which are related to data integration and data transformation processes.

• ADF questions are related to Azure Data Factory, a cloud-based data integration service.

• These questions may involve data pipelines, data flows, activities, triggers, and data movement.

• Candidates may be asked about their experience with designing, monitoring, and managing data pipelines in ADF.

• Exam...read more