Bits of Data Science

9 Data Terms That Every IT Professional Should Know in 2023 1️⃣ Data Warehouse 🏛️: Imagine a grand library of data that's sorted and organized neatly for analysis and reporting. A data warehouse is just that. With its schema-on-write approach, it offers a structured layout - typically star schema or snowflake schema - which makes it perfect for querying historical data and business intelligence. 2️⃣ Data Mart 🏪: Picture a corner store that's part of a larger shopping complex. That's what a data mart is to a data warehouse. It's a specialized section serving the needs of specific business units or teams, speeding up data retrieval and analysis for the domain it serves. 3️⃣ Data Lake 🌊: A data lake is like an enormous reservoir holding a diverse mix of raw data, both structured and unstructured. Because of its schema-on-read approach and its ability to store data in its native format, it's a go-to for big data and machine learning applications. Technologies like Hadoop, Apache Spark, and NoSQL databases help it process huge volumes of data efficiently. 4️⃣ Delta Lake ⏺️: Consider Delta Lake as a safety layer over your data lake. It ensures data integrity with ACID transaction support, allows historical data retrieval through data versioning, and manages small file issues with automatic compaction. 5️⃣ Data Pipeline 🚇: Just as a subway transports people from one place to another, a data pipeline moves data from its source to its destination. It uses ETL or ELT steps to ingest, transform, and deliver data for various analytical uses. Tools like Apache Beam, Airflow, and Kafka are vital in building effective pipelines. 6️⃣ Data Mesh 🕸️: This fresh approach sees data as a product, resolving scalability issues of monolithic architectures by decentralizing data ownership and architecture. With this approach, teams can self-serve their data needs. Principles of microservices architecture and domain-driven design are key in implementing a data mesh. 7️⃣ Data Lakehouse 🏠: A data lakehouse is like a hybrid home that combines the best features of a data warehouse (structured querying capabilities) and a data lake (scalability and flexibility). It uses technologies like Apache Spark and data formats like Parquet and Delta Lake to deliver BI and machine learning capabilities from the same platform. 8️⃣ Data Swamp 🌾: This is a cautionary tale of a data lake gone bad. Without proper management, a data lake can turn into a data swamp, where data is disorganized, inaccessible, and non-compliant, underscoring the importance of proper data governance and quality control. 9️⃣ Data Fabric 🧵: This is the underlying infrastructure designed to manage data end-to-end. It uses AI, machine learning, and semantics to create a dynamic system that can access, discover, transform, and integrate data across different sources, locations, and formats.

The Map of Mathematics

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Important Data Engineering Interview Questions - 🚀 🔹 What is Data Engineering? How does it differentiate from Data Science? 🔹 Can you define ETL? Which are some ETL tools you have used? 🔹 What is a data pipeline? Can you explain its key components? 🔹 Describe data ingestion and its role in data engineering? 🔹 What do you understand by data normalization? Why is it important? 🔹 Could you explain the concept of data partitioning in data engineering? 🔹 What challenges might you face when dealing with Big Data? 🔹 How would you maintain the quality of data in your pipeline? 🔹 Can you discuss some database optimization techniques? 🔹 What are the differences between OLAP and OLTP? 🔹 How would you handle real-time data streaming? 🔹 What are data lakes and data warehouses? How are they different? 🔹 Could you discuss some commonly used data formats? When would you prefer one over another? 🔹 What is Apache Kafka? How is it used in data engineering? 🔹 Explain the role of a Data Catalog in a data engineering framework. 🔹 How would you ensure data privacy and security in your data pipelines? 🔹 How would you handle missing or corrupted data in a dataset? 🔹 What is Apache Spark? How is it used in data engineering? 🔹 How would you handle the processing of a large dataset that cannot fit into the memory of a single machine? 🔹 Can you explain what Lambda & Kappa Architecture is and its components? 🔹 What are the different types of NoSQL databases? When would you use each one? 🔹 Can you explain data serialization and its importance in data engineering? 🔹 How do you ensure data consistency in a distributed system? 🔹 What is the difference between a data lake, a data warehouse, and a data mart? 🔹 What is the role of an ORM in data engineering? 🔹 How do you monitor the performance of a data pipeline? 🔹 What is event-driven processing and why is it important in data engineering? 🔹 What is eventual consistency in a distributed database system? 🔹 Explain the differences between batch processing and stream processing. 🔹 How would you ensure data durability in your data pipeline? 🔹 What is schema-on-read versus schema-on-write? 🔹 What is data wrangling or data munging?

Pandas Vs SQL operations Credit: avichawla.substack.com