Databricks Fundamentals QUES TIONS WITH 100% SOLUTIONS LATEST UPDATE 2023

Databricks Fundamentals QUES TIONS WITH 100% SOLUTIONS LATEST UPDATE 2023 What does Databricks help organizations do? - ANSWER The Databricks Lakehouse Platform enables organizations to: Ingest, process, and transform massive quantities and types of data Explore data through data science techniques, including but not limited to machine learning Guarantee that data available for business queries is reliable and up to date Provide data engineers, data scientists, and data analysts the unique tools they need to do their work Overcome traditional challenges associated with data science and machine learning workflows (we will explore this in detail in our next lesson) As data practitioners work to design their organization's big data infrastructure, they often ask and need to answer questions like: - ANSWER Where/how will we store our big data? How can we process batch and stream data? How can we use different types of data together in our analyses (unstructured vs. structured data)? How can we keep track of all of the work we're doing on our big data? Data lakehouses have the following key features: - ANSWER Transaction support to ensure that multiple parties can concurrently read or write data Data schema enforcement to ensure data integrity (writes to a table are rejected if they do not match the table's schema) Governance and audit mechanisms to make sure you can see how data is being used BI support so that BI tools can work directly on source data - this reduces data staleness. Storage is decoupled from compute, which means that it is easier for your system to scale to more concurrent users and data sizes. Openness - Storage formats used are open and standard. Plus, APIs and various other tools make it easy for team members to access data directly. Support for all data types - structured, unstructured, semi-structured End-to-end streaming so that real-time reporting and real-time data can be integrated into data analytics processes just as existing data is Support for diverse workloads, including data engineering, data science, machine learning, and SQL analytics - all on the same data repository.