1 2 月 16 日， Databricks 宣布完成 超过 40 亿美元 融资，投后估值 1340 亿美元 ；本轮由 Insight Partners 、 Fidelity Management & Research 、 J.P. Morgan Asset Management 领投， Andreessen Horowitz 、 BlackRock 、 Blackstone 等参与。公司表示，本轮距离上一轮融资（当时估值 约 1000 亿美元 ）不到半年。经营数据方面，公司披露其第三季度对应的 年化营收规模超过 48 亿美元 ，同比增长 超过 55% ；同时称其 AI 相关 产品与数据仓库业务的 年化收入规模均超过 10 亿美元 ，并在过去 12 个月实现 自由现金流为正 。 02 为什么是 Databricks? 十年前， 真正能拉开差距的往往是资本和规模：前者支撑持续投入，后者沉淀为渠道、产能、销售体系与品牌。 今天这套逻辑仍有效，但不够。越来越多行业的领先来自两类新资源： 数据和人才。数据决定产品迭代、客户理解与运营效率；人才决定数据能否被 转化为决策与业务结果。 现实问题是，很多企业仍用上个周期的数 ...

Group 1 - Data engineering is defined as the process of designing, building, and maintaining systems that collect, store, analyze data, and make decisions based on that data [2] - Data engineering is essential for data-driven companies, serving as the foundation for data collection to decision-making [1][2] - Understanding the basic principles of data engineering is crucial for better comprehension of the field [3] Group 2 - Data sources can be categorized into structured, semi-structured, and unstructured data sources [5][10] - Structured data sources follow a predefined schema, such as relational databases, CRM systems, and ERP systems [7][9] - Semi-structured data sources include JSON files, XML files, HTML documents, and emails [10][12][15] - Unstructured data sources consist of text documents, social media posts, videos, and images [16][19][21] Group 3 - Data extraction methods include batch processing and real-time streaming [22][24] - Batch processing collects and processes data at scheduled intervals, while real-time streaming involves continuous data collection and processing [24][25] Group 4 - Data storage systems include databases, data lakes, and data warehouses [27][30] - Databases are organized collections of data suitable for transactional systems, while data lakes store raw data in its original format [29][30] - Data warehouses are optimized for querying, analysis, and reporting [30] Group 5 - Data governance and security have become increasingly important, with regulations like GDPR and CCPA emphasizing data integrity and privacy [34] - Data governance includes policies and procedures to ensure data quality, availability, and compliance with regulations [34][36] Group 6 - Data processing and transformation are necessary to clean and prepare data for analysis [37] - ETL (Extract, Transform, Load) processes are critical for integrating data from various sources [41] Group 7 - Data integration involves combining data from multiple sources into a single data repository [44] - Techniques for data integration include ETL, data federation, and API integration [46][47] Group 8 - Data quality is crucial for accurate analysis and decision-making, with validation techniques ensuring data accuracy [57][58] - Continuous monitoring and maintenance of data quality are essential for organizations [66] Group 9 - Data modeling techniques include conceptual, logical, and physical data modeling [70][71] - Data analysis and visualization tools help in ensuring data accuracy and discovering insights [73] Group 10 - Scalability and performance optimization are key challenges in data engineering, especially with growing data volumes and complexity [75][77] - Techniques for optimizing data systems include distributed computing frameworks, cloud-based solutions, and data indexing [79] Group 11 - Current trends in data engineering include the integration of AI and machine learning into workflows [84] - Cloud computing and serverless architectures are becoming standard practices in data engineering [85] Group 12 - The demand for data engineering skills is expected to increase as companies invest in data infrastructure and real-time processing [86]