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Explore the Data Lakehouse concept, its comparison to traditional SQL warehouses, and its significance in modern data architecture.
Learn to orchestrate and schedule complex multi-task processes using Databricks workflows, a powerful extension to the Jobs service that utilizes DAG architecture for efficient data processing and automation.
Discover essential emerging technologies in Data Analytics and learn how to evaluate them for your career advancement.
Learn to make REST API calls to Talkwalker using Python and save results to SQLite. Covers API documentation, demo, master table creation, and upsert operations.
Optimize SQL Endpoints with Databricks Fast Lanes for improved performance and cost-efficiency in analytics and data warehouse workloads.
Explore SparkR for integrating R with Databricks and Apache Spark, covering architecture, APIs, libraries, and practical examples of data manipulation and analysis.
Learn the fundamentals of Databricks and Apache Spark, exploring scale-up vs. scale-out concepts and laying the groundwork for mastering these powerful data processing tools.
Learn to maintain current data in Oracle using Merge statements, integrating with pandas for efficient data updates, inserts, and deletions in Python.
Learn to efficiently update SQL Server tables using the Merge statement and pandas integration, ensuring data stays current for analysis. Covers inserting, updating, and deleting rows with practical demonstrations.
Learn efficient data maintenance in PostgreSQL using Upsert. Master inserting, updating, and deleting rows to keep your data current and optimize your data analysis workflow.
Explore PySpark and RDDs in Databricks: Learn about lazy evaluation, transformations, and actions while analyzing data using Resilient Distributed Datasets.
Learn to leverage Spark SQL window functions for advanced data engineering on Databricks, including cumulative totals, ranking, and aggregations alongside detail rows, enhancing your data manipulation capabilities.
Learn to create and utilize SQL views in Databricks for efficient data analysis and pipeline reuse. Explore common table expressions, aggregations, and case statements for advanced querying techniques.
Explore Spark SQL's powerful features for data engineering, including query optimizations and support for Python, R, and Scala, enhancing parallel processing performance.
Explore the data science process from project inception to completion, covering key steps for successful implementation in Databricks and Apache Spark projects.
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