Data Engineering Basics:
Data Engineering involves designing, constructing, and maintaining systems that collect store, and process data for analysis and reporting.
Data engineers focus on building and maintaining the infrastructure to handle and process data, while data scientists focus on analyzing and deriving insights from the data.
ETL stands for Extract, Transform, Load. It involves extracting data from various sources, transforming it to fit the target data model, and then loading it into a data warehouse or database.
Apache NiFi, Apache Spark, Talend, Apache Airflow, Informatica, Microsoft SSIS, etc.
A data pipeline is a sequence of processes and operations that move data from source to destination, often involving extraction, transformation, and loading.
Data warehousing is the process of collecting, storing, and managing data from various sources to support business intelligence and reporting.
Data partitioning involves dividing a large dataset into smaller, more manageable segments based on certain criteria, often improving query performance.
Data warehouses store structured data for analytical querying, while data lakes store raw and unstructured data for various data processing tasks.
Data normalization reduces data redundancy and improves data integrity by organizing data into separate tables to minimize duplication.
The CAP theorem states that in a distributed data system, you can’t have all three of Consistency, Availability, and Partition tolerance simultaneously.
Sharding involves distributing a large database across multiple servers to improve performance and scalability.
NoSQL databases are designed for unstructured or semi-structured data and offer better scalability, while relational databases are suitable for structured data and provide strong consistency.
— Hadoop is an open-source framework for processing and storing large datasets across a distributed cluster of computers.
— MapReduce is a programming model used to process and generate large datasets that can be parallelized across a distributed cluster.
Apache Spark is an open-source data processing and analytics engine that provides fast in-memory data processing capabilities.
Batch processing involves processing data in large volumes at scheduled intervals, while stream processing deals with data in real-time as it’s generated.
Data compression reduces storage requirements and can improve data processing speed by reducing the amount of data that needs to be read from storage.
A data model is a conceptual representation of how data is organized, stored, and accessed in a database system.
Both are data warehousing techniques. Star schema has a centralized fact table connected to dimension tables, while snowflake schema normalizes dimension tables for reduced redundancy.
Schema evolution refers to the ability to modify the structure of a database schema over time while preserving existing data and applications.
A surrogate key is a unique identifier assigned to a record in a database, often used as a primary key, especially when the natural key is complex or prone to change.
Data federation is the process of integrating data from multiple sources in real-time to provide a unified view without copying the data into a central repository.
CDC is a technique used to identify and capture changes made to a database so that these changes can be propagated to other systems or databases.
Data virtualization allows users to access data from various sources without the need to physically move or replicate the data.
Data quality ensures that the data used for analysis and decision-making is accurate, complete, and reliable.
Duplicate records, missing values, inconsistent formatting, outdated information, etc.
27. How can you address data quality issues?
Data Governance and Security:
Data governance involves defining policies, procedures, and standards for managing and ensuring the quality and security of data.
Data masking is the process of obfuscating sensitive data in a way that retains its original format but hides the actual values.
Encryption is the process of converting data into a code to prevent unauthorized access. It’s important to protect sensitive data during storage and transmission.
Big Data Technologies:
Kafka is a distributed event streaming platform used for building real-time data pipelines and streaming applications.
HBase is a distributed NoSQL database that’s optimized for random read and write operations on large datasets.
Cassandra is a distributed NoSQL database designed to handle large amounts of data across multiple nodes with high availability and scalability.
Cloud Data Engineering:
Scalability, flexibility, reduced infrastructure management, pay-as-you-go pricing, and easier integration with other cloud services.
AWS Glue is a managed ETL service that makes it easy to move data between various data sources and data warehouses.
Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines.
Use indexing, data partitioning, caching, and distributed processing frameworks to improve query and processing speed.
Data denormalization involves storing redundant data to improve query performance at the expense of increased storage requirements.
Machine Learning Integration:
Data engineers provide clean, well-structured data to data scientists for training and testing machine learning models.
Feature engineering is the process of creating new features or transforming existing features to enhance the performance of machine learning models.
Version Control and Collaboration:
Version control helps track changes to code, scripts, and configurations, facilitating collaboration and ensuring reproducibility.
Git is a version control system that tracks changes to code and files. It’s used to manage scripts, configurations, and code related to data pipelines.
Eventual consistency is a property of distributed systems where data changes will eventually propagate through the system, ensuring all replicas converge to the same state.
A distributed cache is a system that stores frequently accessed data in memory across multiple nodes to improve data access speed.
Coding and Scripting:
Python, Java, Scala, and SQL are commonly used for data engineering tasks.
Use techniques like schema evolution, versioning, and data transformation to accommodate schema changes without breaking the pipeline.
Job Scheduling and Automation:
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows and data pipelines.
Job scheduling ensures that data pipelines, ETL processes, and other tasks run at specified times or triggers, improving automation and consistency.
Troubleshooting and Debugging:
Monitor resource utilization, identify bottlenecks, profile query execution, and optimize data processing steps.
Check for missing or duplicate records, validate data sources, inspect data transformations, and collaborate with data source owners.
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