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Home / Blog / Interview Questions on Data Engineering / Top 35 Data Architect 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 Architecture refers to the models, policies, rules, and standards governing the collection, storage, arrangement, and use of data in organizations. It's crucial for ensuring data is managed effectively and aligns with business goals.
Typical layers include data sources, data ingestion, data storage, data processing, data orchestration, data services/APIs, and data consumption.
Data lakes store vast amounts of raw data in its native format. They are a flexible layer in Data Architecture, allowing for storage of structured, semi-structured, and unstructured data.
A central store for integrated data of several sources is called a data warehouse. It functions is a structured storage layer in data architecture that is enhanced for reporting and analysis.
Data modeling is crucial for designing the data structures and schema within databases and warehouses. It ensures that data is organized logically and efficiently for access and analysis.
The process of obtaining data from several sources, changing it into an appropriate format, and then putting it into a data warehouse and other storage system is known as ETL (Extract, Transform, Load).
The data ingestion layer is responsible for importing data from various sources into the system. It handles the initial collection, extraction, and transportation of data.
Data pipelines, which usually involve operations including ingestion, transformation, and loading, automate the movement of data from its source to its destination. They are essential to the effective processing and transportation of data.
Data orchestration coordinates various data processing tasks, ensuring they occur in the correct order and managing dependencies between different data flows.
Real-time data processing involves the continuous input, processing, and output of data. It's crucial for scenarios where immediate processing and insights are required.
APIs (Application Programming Interfaces) are used to enable integration between different systems and layers in Data Architecture, allowing different components to communicate and exchange data.
Data security considerations include encryption, access controls, compliance with data protection regulations, and implementing secure data transmission and storage practices.
Cloud computing offers scalable, on-demand resources and services for data storage, processing, and analysis, allowing for more flexible and cost-effective Data Architectures.
Data federation is the process of aggregating data from disparate sources to create a single, virtual view. It allows for unified data access without physically integrating data.
Metadata provides information about data, like its source, format, and structure. In Data Architecture, it's vital for understanding, managing, and using data effectively.
Data governance involves managing the availability, integrity, and security of the data. It's integrated into Data Architecture through policies, standards, and procedures that guide data management.
MDM systems ensure the uniformity, accuracy, stewardship, and consistency of an enterprise's official shared master data. They integrate into Data Architecture as a central source of master data.
Challenges include handling the volume, variety, and velocity of Big Data, ensuring data quality, integrating diverse data sources, and scaling data infrastructure.
Data virtualization creates a virtual layer that provides unified data access and retrieval across different sources, without needing to move or replicate data.
BI tools and systems are incorporated into Data Architecture for analyzing data, generating reports, and supporting decision-making processes based on data stored in warehouses or lakes.
Scalability ensures that the data infrastructure can handle growth in data volume, velocity, and variety, maintaining performance and avoiding system overloads.
NoSQL databases handle a variety of data formats and are designed for high scalability and flexibility. They are used in Data Architectures for unstructured or semi-structured data.
The convergence of data lakes and warehouses combines the flexible storage and processing of a data lake with the structured environment of a data warehouse, enhancing analytical capabilities.
Data marts are subsets of data warehouses tailored to specific business lines or departments. They fit into Data Architecture as focused areas for specific analytics needs.
Event-driven architectures trigger actions and data processing in response to events. They influence Data Architecture by introducing real-time data processing and responsive data flows.
AI and machine learning require Data Architectures to support large datasets, complex analytics, and real-time processing, influencing design and technology choices.
Data quality is maintained through validation rules, consistency checks, data profiling and cleansing, and ensuring accurate data transformations and mappings.
Disaster recovery plans are essential to ensure data availability and continuity of operations in case of system failures, data corruption, or other disasters.
Best practices include regular backups, off-site storage, using reliable backup systems, and testing recovery processes to ensure data can be restored effectively.
Addressing data latency involves optimizing data processing and transfer processes, using faster storage solutions, and implementing efficient data caching strategies.
Data compression reduces the size of data, making storage more efficient and improving transfer speeds. It's important for managing large volumes of data.
Integration involves connecting and harmonizing external data with internal systems, often using APIs, ETL processes, or data federation techniques.
Data de-duplication involves identifying and removing duplicate records, improving data quality and reducing storage requirements.
Streaming data involves continuous data flow and processing, commonly used in real-time analytics. It's integrated into Data Architecture to enable immediate insights and actions.
Trends include the increasing adoption of cloud services, the growth of edge computing, advancements in AI and machine learning, and the increasing importance of data privacy and security.
360DigiTMG - Data Analytics, Data Science Course Training in Chennai
1st Floor, Santi Ram Centre, Tirumurthy Nagar, Opposite to Indian Oil Bhavan, Nungambakkam, Chennai - 600006
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