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This topic covers the end-to-end process of building and maintaining data pipelines. Learn about ETL, data warehousing, data modeling, and data quality. Explore tools like Databricks and Spark for efficient data processing. Gain skills to design robust and scalable data solutions for various business needs.
Prep for the Databricks Data Engineer certification! Hands-on training to master skills and ace the exam.
Derar Alhussein | 8x Databricks CertifiedLearn data engineering with Databricks! Build robust pipelines using Spark, Delta Lake, and cloudFiles. Master data processing & storage.
Durga Viswanatha Raju GadirajuLearn Databricks Data Engineering! Hands-on training & practical examples to master Databricks skills. Become a Data Engineer.
Ankit Mistry : 200,000+ StudentsMaster Talend for Big Data, Cloud & ELT. Learn SCD, Java, Amazon, Kafka & MongoDB. Boost performance with this advanced course!
Samuel LenkMaster Flow Files, back pressure, and data prioritization. Learn data provenance & real-world implementation with hands-on practice.
FutureX SkillsMaster AWS Data Lakes! Learn big data, cloud data engineering, and data architecture skills. Accelerate your career with practical data lake expertise.
Nikolai SchulerMaster modern data architectures: Data Warehouses, Lakes, Lakehouses & Mesh. Learn Kafka, Lambda/Kappa, Feature Stores, Vector DBs & more. Build scalable data solutions!
Biju KrishnanLearn data integration with SSIS! Perfect for BI developers & database pros. No prior experience needed. Master data movement & transformation.
Stacia Misner VargaMaster Azure data solutions! Learn data lakes, ETL with ADF, Synapse Analytics, and big data pipelines using Databricks. Build powerful data workflows.
Durga Viswanatha Raju GadirajuLearn Hive query language for Big Data! Master Hadoop, MapReduce & Hive 0.11 features to solve real-world data challenges. Distributed computing skills included.
Ahmad AlkilaniMaster Google Cloud data skills! Learn BigQuery, Dataproc, Dataform & more. Become a Data Practitioner with Dataflow & Looker Studio.
Ankit Mistry : 200,000+ StudentsLearn Kestra workflow orchestration with this hands-on course. Automate tasks & streamline processes. Get practical skills in workflow management.
Shruti MantriLLM features, RAG, agents and automation wired into a real product — built by the devs who shipped this site.
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This topic covers building robust data pipelines using tools like SQL, Python, and Airflow. Learn to design, develop, and deploy automated workflows for data ingestion, transformation, and loading. Gain skills in data quality, scheduling, and monitoring to ensure reliable and efficient data processing.
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Explore core data science methods and techniques for effective analysis and modeling. This includes statistical analysis, machine learning algorithms (regression, classification, clustering), data preprocessing, and model evaluation. Learn to apply these methods to real-world datasets and gain insights for data-driven decision-making. Focuses on practical application and future trends.
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Explore the fundamentals of Big Data, encompassing data analysis, storage, and processing techniques. This topic covers key technologies like Hadoop and Spark, focusing on handling large datasets. Learn about data pipelines, distributed computing, and extracting valuable insights from complex data sources. Develop skills for real-world Big Data applications.
Essential skills for Data Engineering include programming (Python, SQL), data warehousing, ETL processes, and cloud platforms like AWS or Azure.
Data Engineering focuses on building and maintaining data infrastructure, while Data Science uses that data to perform analysis and build predictive models.
Data Engineering prepares data for machine learning by building pipelines to ingest, transform, and store data efficiently for model training and deployment.
Common tools in Data Engineering pipelines include Apache Kafka, Spark, Hadoop, and cloud-based data warehousing solutions like Snowflake or Redshift.
Data Engineering enables businesses to reliably access and utilize data, leading to better decision-making, improved operational efficiency, and data-driven insights.