Download to read offline
Data Engineering Overview for new learners.pptx
- 1. Data Engineering Overview AnInsight into Data Engineering and its Distinction from Data Science
- 2.
- 3. Data Engineering vsData Science 01
- 4. Data Engineering focuseson the design and construction of systems for collecting, storing, and analyzing data. Data Engineers are responsible for building infrastructure, data pipelines, and ensuring data integrity. In contrast, Data Science involves interpreting and analyzing data to extract insights. Data Scientists utilize statistical and analytical techniques to propose solutions based on data trends. Definitions and Roles
- 5. Data Engineering ismore about the technology, tools, and processes that enable the movement and processing of data. Data Science, however, centers around modeling and deriving insights. Data Engineers deal with ETL (Extract, Transform, Load) processes, while Data Scientists focus on hypothesis testing and predictive modeling. Key Differences
- 6. Real-world Applications Data Engineering iscrucial in sectors like finance and healthcare for data management and reporting systems. Data Science is applied in marketing for customer segmentation, in finance for risk assessment, and in healthcare for predictive analytics to improve patient outcomes.
- 7.
- 8. Databricks is aunified analytics platform that combines data engineering and data science workflows. It is built on Apache Spark and provides a collaborative environment for data teams. Databricks enables seamless integration with various data sources and simplifies the process of developing and maintaining data pipelines, making it an essential tool for modern data engineering. Platform Overview
- 9. Core Features Key featuresof Databricks include an interactive workspace, support for various programming languages (including Python, R, Scala, and SQL), automated cluster management, and advanced collaboration tools like notebooks. Additionally, Databricks provides built- in version control and supports the integration of Machine Learning and AI workflows to enhance data analysis capabilities.
- 10. Databricks is usedin various industries for data transformation, ETL processes, and real-time analytics. It enables organizations to streamline their data workflows, from ingestion to processing and analysis. Companies leverage Databricks for data lake management, batch processing, and on-demand analytics, which allows for timely insights and data-driven decision-making. Use Cases in Data Engineering
- 11.
- 12. Apache Spark isan open-source distributed computing system designed for fast data processing. It offers an interface similar to MapReduce but is optimized for speed and efficiency. Spark is capable of handling both batch and real-time data processing tasks, making it highly versatile for handling large datasets across a cluster of machines. Introduction to Spark
- 13. Resilient Distributed Datasets(RDDs) are the core data structure of Spark, providing fault tolerance and parallel processing capabilities. DataFrames, on the other hand, are a higher-level abstraction built on RDDs, allowing for more optimized performance and easier manipulation of structured data using SQL-like operations. RDDs and DataFrames
- 14. Common APIs and Libraries Sparkprovides several APIs for different programming languages, enabling developers to interact with its functionalities. Common libraries within Spark include Spark SQL for querying structured data, MLLib for machine learning tasks, and GraphX for graph processing. These libraries enhance the capabilities of Spark, allowing comprehensive data analytics and processing.
- 15.
- 16. CREDITS: This presentationtemplate was created by Slidesgo, and includes icons, infographics & images by Freepik Thank you! Do you have any questions? +00 000 000 000