NoSQL Databases – Lectures 2017

Course Overview | Lectures | Seminars | Group Projects | Sources
  • Lecture 1 (2/21/2017): Why NoSQL, Principles, Overview, Course organization – slides
    • content: Motivation for NoSQL databases (Big Data, Big Users, Cloud Computing, Horizontal scalability); Value of Relational databases; General principles of NoSQL databases; Types of NoSQL databases (basic characteristics, uses cases, representatives); One example: Database technologies behind Facebook;
    • covered terms: Big Data (Volume, Velocity, Variety), OLTP/OLAP/RTAP, RDBMS, ACID, Aggregate-oriented data models, Key-value stores, Document databases, Column-family stores, Graph databases
  • Lecture 2 (2/28/2017): Distributed Computing with MapReduce – slides
    • content: Distributed File Systems, Google File System (GFS), MapReduce programming model; MapReduce Framework; Apache Hadoop ecosystem; Apache Spark
    • covered terms: Distributed File Systems: GFS, chunk server; MapReduce: Map, Combine, Grouping/Shuffling, Reduce; Hadoop Distributed File System (NameNode, DataNode, HeartBeat, BlockReport); Apache YARN, JobTracker, TaskTracker
  • Lecture 3 (3/7/2017): Principles of NoSQL Databases: Data Model, Distribution & Consistency – slides
    • content: Basic Principles of NoSQL Databases – Aggregate data model, horizontal scaling, relaxing consistency; Models of Data Distribution; Consistency in databases, transactions; Relaxing consistency in distributed databases – theories and techniques; relaxing durability;
    • covered terms: aggregate data model, vertical/horizontal scalability (scaling up/out), sharding, replication (master-slave, peer-to-peer), read/write/replication consistency, CAP Theorem, eventual consistency, BASE, Quorums
  • Lecture 4 (3/21/2017): Distributed Key-value Stores – slides
    • content: Key challenges and solutions: data sharding, data balancing, replica management, management of nodes; Comparison of Individual Stores: features to consider, connecting to database;Fundamentals; Suitable Use Cases; Basic Example (Riak)
    • covered terms: Amazon Dynamo, consistent hashing, virtual nodes, version stamps (counter, GUID, hash, timestamp), vector stamps (Lamport timestamps, vector clocks, version vectors, matrix clocks), anti-entropy, read repair, gossip protocols, two-phase commit protocol (2PC), multi-version concurrency control (MVCC), levels of isolation, skew write anomaly
  • Lecture 5 (3/28/2017): Key-value Stores II: Embedded, Distributed, and In-memory Stores – slides
    • content: embedded stores: LevelDB; Distributed key-value stores: Riak, Infinispan; in-memory caches: Memcached. Serialization: Protocol Buffers, Apache Thrift
    • covered terms: Log-structured Merge-Tree (LSM Tree), SSTable; Riak Links, Indexes, Search; memory cache, data eviction, distributed transaction management (X/Open XA), Lucene (Solr); Memcached; object serialisation (marshalling), Protocol Buffers, Apache Thrift
  • Lecture 6 (4/4/2017): Document Databases – slides
    • content: Text Data Formats; Document Databases: Usage and Principles Behind, MongoDB: Data Models, Querying, Updates, Indexes, BSON, Distribution, MapReduce, Journaling, Transactions
    • covered terms: JSON, XML; MongoDB
  • Lecture 7 (4/11/2017): Column-family Stores – slides
    • content: Column Family Data Model, System Architectures; Cassandra: CQL, Data Partitioning & Replication, Local Data Persistence, Queries
    • covered terms: Google BigTable, Cassandra, HBase, column family, super columns, CQL, memtables, SSTable, lightweight transactions
  • Lecture 8 (4/18/2017): Graph Databases – slides
    • content: Graph Databases: Mission, Data, Example; Graph Theory: Representations, Data Locality, Graph Partitioning and Traversal; Types of Queries; Transactional Databases; Neo4j: Basics
    • covered terms: Directed/undirected graphs, Adjacency Matrix, Adjacency List, Incidence Matrix, Laplacian Matrix; Breadth-first Search (BFS), BFS Layout, Bandwidth minimization problem, Graph Partitioning (1D, 2D); Sub-graph, Super-graph, Similarity Queries; (Non-)Mining-Based Graph Indexing Techniques; Neo4j
  • Lecture 9 (4/25/2017): Presentation of Projects (I)
    • content: presentation of four group projects
  • 2nd May: Lecture canceled
  • Lecture 10 (5/9/2017): Presentation of Projects (II)
    • content: presentation of four group projects
  • Lecture 11 (5/16/2017): Invited Talk: Tomáš Komenda, Seznam.cz + Presentation of Projects (III)
    • content: Apache Solr at Seznam.cz or Impala + Hive at Seznam.cz and presentation of two remaining group projects