Big Data For Architects
This course not only covers all the basic Big Data tools and technologies but also covers the in detail differentiation between the same set of technologies.
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Segment - 01 - Course Structure and Approach
Segment - 02 - Pre-requisites
Segment - 03 - Course Audience
Segment - 04 - About Instructor
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Segment - 05 - Google Cloud Account Setup
Segment - 06 - Creating a Dataproc Cluster
Segment - 07 - GCP Account Best Practices
Installation DataProc cluster
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Segment - 08 - Big Data Logical Architecture
Segment - 09 - Evolution of Big Data Technologies
Segment - 10 - Key Big Data Architectures
Segment - 11 - Typical Big Data Batch Pipeline
Segment - 12 - Typical Big Data Streaming Pipeline
Segment - 13 - Bonus 1 - Another Example of Big Data Streaming Pipeline
Segment - 14 - Bonus 2 - Another Example of Big Data Streaming Pipeline
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Segment - 15 - Factors to consider while comparing Ingestion frameworks
Segment - 16 - Kafka vs Flume
Segment - 17 - NiFi vs Kafka
Segment - 18 - Sqoop vs Flume
Segment - 19 - Sqoop vs Kafka Connect
Segment - 20 - Hands-on NiFi Installation
Segment - 21 - Hands-on Kafka Installation
Segment - 22 - Hands-on Kafka and NiFi Integration Background
Segment - 23 - Hands-on Kafka and NiFi Integration
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Segment - 24 - Factors to consider while comparing Storage frameworks
Segment - 25 - HDFS vs HBase
Segment - 26 - HBase vs Kudu
Segment - 27 - HDFS vs Kudu
Segment - 28 - HBase vs Cassandra
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Segment - 29 - Text vs Binary
Segment - 30 - Interoperability
Segment - 31 - Row Oriented vs Column Oriented
Segment - 32 - Splittable Formats
Segment - 33 - Schema Evolution
Segment - 34 - Comparing Data Formats
Segment - 35 - Hands-on Sqoop Installation on Dataproc Cluster
Segment - 36 - Hands-on Big Data Batch Pipeline Use Avro Format
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Segment - 37 - Factors to consider while comparing Processing frameworks
Segment - 38 - MR vs Spark Logical Architecture Perspective
Segment - 39 - MR vs Spark Performance Perspective
Segment - 40 - Spark vs Tez
Segment - 41 - Spark vs Flink
Segment - 42 - Kafka Streams vs Spark Streaming
Segment - 43 - Spark 2.x Streaming vs Spark 1.x Streaming
Segment - 44 - Spark Core vs Spark SQL
Segment - 45 - Hands-on Kafka & Spark Streaming Integration
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Segment - 46 - Factors to consider while comparing Analysis frameworks
Segment - 47 - Hive vs Impala
Segment - 48 - Hive vs Pig
Segment - 49 - Hive vs Spark SQL
Segment - 50 - Hive vs Hive LLAP vs Impala
Segment - 51 - Hive vs KSQL
Segment - 52 - 7. KSQL vs KSQLDB
Segment - 53 - Hands-on KSQL
Segment - 54 - Hands-on Write to a Stream and Table using KSQL
Segment - 55 - Hands-on Streaming ETL Pipeline Background
Segment - 56 - Hands-on Build a Scalable ETL Pipeline with Kafka Connect - part 1
Segment - 57 - Hands-on Build a Scalable ETL Pipeline with Kafka Connect - part 2
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Segment - 58 - Delta Architecture
Segment - 59 - Why Delta Lake?
Segment - 60 - Challenges with Data Lake
Segment - 61 - Delta Lake Demo
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Segment - 62 - Solr vs ElasticSearch
Segment - 63 - Cloudera Search vs Solr
Segment - 64 - Oozie vs Airflow
Segment - 65 - KSQL vs KStreams
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Segment - 66 - Conclusion