3.1. The "Vs" pt. 1 | U-Hopper Academy

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Big Data Technologies

LECTURE 1. DATA BASICS

1.1. Introduction: Data and its intrinsic value (7:47)
1.2. From data to knowledge (3:21)
1.3. Data & datasets (4:26)
1.4. Types of data (15:16)
1.5. Processing data (5:25)
SELF-ASSESSMENT TEST (1)

LECTURE 2. DATA MODELLING

2.1. Data model vs. data format (24:11)
2.2. Data streams pt. 1 (18:16)
2.3. Data streams pt. 2 (20:49)
2.4. Batch vs. stream processing (an introduction) (17:06)
SELF-ASSESSMENT TEST (2)

LECTURE 3. CHARACTERISTICS OF BIG DATA

3.1. The "Vs" pt. 1 (35:05)
3.2. The "Vs" pt. 2 (9:46)
3.3. Big Data vs. Small Data (3:10)
3.4. Getting value out of Big Data (13:34)
3.5. Big Data Strategy (7:22)
SELF-ASSESSMENT TEST (3)

LECTURE 4. LEGAL ASPECTS, SECURITY & PRIVACY

4.1. Legal aspects of data (15:00)
4.2. GDPR (36:14)
4.3. Data portability (3:40)
4.4. Data licensing (6:38)
4.5. Privacy & Security in Big Data (sketch) (7:42)
SELF-ASSESSMENT TEST (4)

LECTURE 5. BIG DATA MANAGEMENT SYSTEMS

5.1. Databases: an introduction (5:40)
5.2. Relational vs. Non-relational Databases (5:50)
5.3. Relational Databases (40:52)
5.4. No-SQL Databases (17:49)
5.5. Schema-on-read vs. Schema-on-write (12:15)
SELF-ASSESSMENT TEST (5)

LECTURE 6. BIG DATA RETRIEVAL

6.1. CAP Theorem (13:10)
6.2. Query SQL pt. 1 (8:24)
6.3. Query SQL pt. 2 (27:26)
6.4. Query MongoDB (9:53)
6.5. Query SPARQL (25:26)
SELF-ASSESSMENT TEST (6)

LECTURE 7. STORING BIG DATA

7.1. Introduction (13:24)
7.2. HDFS pt. 1 (15:58)
7.3. HDFS pt. 2 (8:57)
7.4. Data Warehouse (DWH) (7:25)
7.5. DWH Architecture (11:42)
7.6. Data Lake (7:12)
7.7. Object Storage (4:23)
SELF-ASSESSMENT TEST (7)

LECTURE 8. BIG DATA INGESTION

8.1. Ingesting Big Data (7:11)
8.2. Message queues (7:37)
8.3. Pub/Sub (9:41)
8.4. MQ Technologies - MQTT pt. 1 (9:28)
8.5. MQ Technologies - MQTT pt. 2 (7:10)
8.6. MQ Technologies - Kafka pt. 1 (8:13)
8.7. MQ Technologies - Kafka pt. 2 (12:02)
8.8. MQ Technologies - Kafka pt. 3 (10:56)
SELF-ASSESSMENT TEST (8)

LECTURE 9. BATCH PROCESSING

9.1. Processing Big Data (2:41)
9.2. MapReduce pt. 1 (6:57)
9.3. MapReduce pt. 2 (6:23)
9.4. MapReduce pt. 3 (8:51)
9.5. Apache Spark pt. 1 (13:16)
9.6. Apache Spark pt. 2 (17:09)
9.7. Apache Spark pt. 3 (6:36)
9.8. Apache Spark pt. 4 (9:17)
SELF-ASSESSMENT TEST (9)

LECTURE 10. DEEP-DIVE INTO NoSQL

10.1. Categories of NoSQL (1:22)
10.2. Introduction to Redis (5:16)
10.3. Redis: how it works (3:39)
10.4. Redis: data structures (14:47)
10.5. Redis: more data structures (12:33)
10.6. Redis: persistency, replication, clustering (5:18)
10.7. Wide-Column DB (9:07)
SELF-ASSESSMENT TEST (10)

LECTURE 11. STREAM PROCESSING

11.1. Spark Streaming pt. 1 (8:47)
11.2. Spark Streaming pt. 2 (9:44)
11.3. Spark Streaming pt. 3 (8:47)
11.4. Introduction to Apache Flink (10:49)
11.5. Apache Flink: basics (9:04)
11.6. Apache Flink: APIs (8:10)
11.7. Apache Flink: Streaming dataflows (16:44)
11.8. Apache Flink: Fault tolerance (16:35)
11.9. Lambda architecture (8:18)
SELF-ASSESSMENT TEST (11)

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