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Deep Dive into Spark SQL with Advanced Performance Tuning Wenchen Fan (Databricks) and Xiao Li (Databricks)
⏲ 18 min 83 sec ✓ 11-Jun-2018
Apache Kafka in 6 minutes
⏲ 6 minutes 48 seconds 👁 466.9K
3. Apache Kafka Fundamentals | Apache Kafka Fundamentals
⏲ 24 minutes 14 seconds 👁 219K
The Myth of the Magical Messaging Fabric: Jakub Korab
⏲ 35 min 47 sec ✓ 08-Sep-2016
Kafka in 100 Seconds
⏲ 2 minutes 35 seconds 👁 395.7K
Apache Kafka 101: Introduction (2023)
⏲ 5 minutes 9 seconds 👁 70.2K
Fast data made easy with Apache Kafka and Apache Kudu (incubating)
⏲ 23 min 34 sec ✓ 13-May-2016
System Design: Apache Kafka In 3 Minutes
⏲ 3 minutes 46 seconds 👁 63K
What is Kafka?
⏲ 9 minutes 17 seconds 👁 218.2K
Docker Chicago - 2016-08-11 - Ken Sipe - No One Puts Java in a Container
⏲ 37 min 78 sec ✓ 16-Aug-2016
Kafka Tutorial - Core Concepts
⏲ 13 minutes 4 seconds 👁 454.7K
Docker Compose Basics: Connect Spring Boot to MySQL and Kafka | Docker Tutorial
⏲ 1 hour 7 minutes 44 seconds
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_deep-dive-into-spark-sql-with-advanced-performance-tuning-wenchen-fan-databricks-and-xiao-li-databricks.webp "Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark. Spark SQL can process, integrate and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will get a deeper understanding of S")
_apache-kafka-in-6-minutes.webp "James Cutajar")
_3-apache-kafka-fundamentals-124-apache-kafka-fundamentals.webp "Confluent")
_the-myth-of-the-magical-messaging-fabric-jakub-korab.webp "Messaging systems underpin architectures ranging from stream processing, through to event sourcing and communication between loosely-coupled microservices. To most developers the technology is not core to the business they work in, and is often disregarded as “just a pipe” for message distribution. Without understanding their capabilities and underlying design decisions, we tell ourselves myths how these systems “should” work and are frustrated in production by behaviours coming from des")
_kafka-in-100-seconds.webp "Fireship")
_apache-kafka-101-introduction-2023.webp "Confluent")
_fast-data-made-easy-with-apache-kafka-and-apache-kudu-incubating.webp "Historically, use cases such as time series and mutable-profile datasets have been possible but difficult to achieve efficiently using traditional HDFS storage engines. These solutions might involve complex ingestion paths, deep understanding of file types, and compaction strategies. With the introduction of Kudu, many of these difficulties are eliminated. At the same time, interest in streaming solutions and low-latency analytics has surged with the growing popularity of tools like Apache Kafka")
_system-design-apache-kafka-in-3-minutes.webp "ByteByteGo")
_what-is-kafka.webp "IBM Technology")
_docker-chicago-2016-08-11-ken-sipe-no-one-puts-java-in-a-container.webp "Ken Sipe takes us through a look at the anatomy of a container including control groups (cgroups) and namespaces. Then we will take a deep dive on Java's memory management and GC characteristics and how JRE characteristics change based on core count. The session will detail specifically the difference between CPU sets and CPU shares and memory management. The session will close with a deep understanding of the consequences of running the JRE in a CPU share environment and the potential for pseud")
_kafka-tutorial-core-concepts.webp "Learning Journal")
_docker-compose-basics-connect-spring-boot-to-mysql-and-kafka-124-docker-tutorial.webp "Globant")
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