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Home / Blog / Interview Questions on Data Engineering / Top 35 Data Source Interview Questions
Sharat Chandra is the head of analytics at 360DigiTMG as well as one of the founders and directors of AiSPRY. With more than 17 years of work experience in the IT sector and Worked as a Data scientist for 14+ years across several industry domains, Sharat Chandra has a wide range of expertise in areas like retail, manufacturing, medical care, etc. With over ten years of expertise as the head trainer at 360DigiTMG, Sharat Chandra has been assisting his pupils in making the move to the IT industry simple. Along with the Oncology team, he made a contribution to the field of LSHC, especially to the field of cancer therapy, which was published in the British magazine of Cancer research magazine.
Table of Content
Data sources are the starting points in data pipelines where data originates. They can include databases, file systems, live data feeds, APIs, and other data storage or generation systems.
Data sources can be categorized as structured (e.g., SQL databases), semi-structured (e.g., JSON, XML), or unstructured (e.g., text documents, images).
Challenges include dealing with different data formats, inconsistent data quality, varying access protocols, and ensuring data security and privacy.
Ensuring data quality involves validating data formats, checking for data completeness and accuracy, and using data cleaning and transformation techniques.
A collection of protocols called the API (Application Programming Interface) is utilised to create and integrate application software. APIs are used in data pipelines to retrieve data from outside systems or services.
Data extraction from webpages is known as web scraping. It is employed in data pipelines for gathering information from non-structured API sources.
Considerations include understanding the schema, using efficient queries, managing load on the database, and handling data consistency and transaction boundaries.
Streaming data sources provide continuous data flow and require real-time processing. Batch data sources provide data in chunks at specific intervals and are processed in batches.
A large volume of unprocessed data in its original format can be stored in a data lake. It provides large-scale, raw data for a variety of analytical purposes, acting as a data source.
Structured data is handled by using standard database queries and ETL processes, ensuring data integrity and optimizing for efficient storage and retrieval.
Common file formats include CSV, JSON, XML, and Parquet. The choice depends on the data structure, size, and intended use in the pipeline.
Data schemas define the structure of data, which is crucial for data validation, transformation, and integration into the pipeline.
Managing changes involves implementing version control, monitoring data source schemas for changes, and using flexible data ingestion and processing methods to accommodate changes.
Data replication involves copying data from one source to another for backup, scalability, or distributed processing. It's used in pipelines for ensuring data availability and load balancing.
IoT devices generate real-time, continuous data streams. They act as sources in data pipelines by providing sensor data, usage metrics, and other telemetry data for analysis.
Best practices include using encryption, implementing access controls, regularly updating and patching systems, and following compliance standards.
Handling unstructured data involves techniques like text analytics, image processing, and natural language processing to extract meaningful information.
Data enrichment involves enhancing, refining, or improving raw data with additional context or information, often by integrating data from additional sources.
Cloud data sources provide scalable, on-demand data storage and services. They integrate with pipelines through cloud-native interfaces and APIs.
A data warehouse serves as a single, organised location for combined, query-and analysis-ready data from several sources. It serves as a source for organised, cleaned, and historical data for sophisticated analytics.
Social media platforms provide a wealth of unstructured data. They are used as sources in pipelines for sentiment analysis, trend monitoring, and consumer behavior insights.
Considerations include data relevance, quality, licensing and compliance issues, and the need for data cleaning and transformation.
Time-sensitive data requires real-time or near-real-time processing, efficient data ingestion methods, and time-stamping for chronological analysis.
CRM (Customer Relationship Management) systems provide valuable customer data. They act as sources by feeding customer interactions, sales data, and preferences into pipelines for analysis.
Data accuracy is validated by cross-referencing with trusted sources, implementing data quality checks, and using data validation rules.
Data transformation involves converting data into a suitable format or structure for analysis. It's necessary for standardizing and harmonizing data from different sources.
Mobile devices provide user data, location data, app usage statistics, and more. They contribute to pipelines by offering real-time, user-centric data for personalized services and analytics.
Big data affects data source management through boosting the amount, speed, and diversity of data, necessitating the use of reliable, scalable, and effective data handling methods.
Log files provide a record of events and transactions. They are used in pipelines for monitoring, security analysis, and understanding user behavior.
Message queues provide a method for asynchronous communication between different parts of a system. In data pipelines, they help manage data flow and load balancing.
Handling data source failures involves implementing redundancy, failover mechanisms, and robust error handling and recovery procedures.
Geospatial data includes location-based data. It's used in pipelines for mapping, spatial analysis, and location-based insights and services.
CDC involves identifying and capturing changes made to data in a source system. It's relevant for ensuring data pipelines have up-to-date and consistent data.
Financial systems provide transactional and market data. They contribute to pipelines by offering insights into financial trends, customer behavior, and compliance monitoring.
Data fabric is an architecture and set of data services providing consistent capabilities across various endpoints in a distributed data environment. It integrates diverse data sources for more accessible, integrated, and efficient data management.
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