1. Storage Systems for Managing Voluminous DataBy
Manoj Krishna Panguluri
Bhavik Mistry
Sandeep Kasavaraju
CS455: Introduction to Distributed Systems
Department of Computer Science , Colorado State University

2. Importance Increase in data from various sources
To process the data, some form of storage is required.
Some areas where this research is importation:
Geographic Information Systems
High Energy Physics
Satellite Imaging

3. Problem Characterization
Information handled is in terms of Petabytes.
Capacity of hard disk not increasing proportionally.
Challenges :
Contiguous Storage
Retrieval from Distributed Storage
Management of data arriving at high rates.
Scalable approach

4. Trade-off Space Problem with relational model leading to NoSQL.
ACID vs BASE
Categories of NoSQL
Key-value Stores
Column-Oriented Databases
Document Store
Graph Database

5. Dominant Approaches Key-Value Store : Dynamo
Highly available, store data on solid state drives.
Different in terms of target requirements.
Column-Oriented Databases :
BigTable
Offers consistency, fault tolerance and persistence.
Three components : Library, Master server, Tablet server.
Offers access control at column family level.
Cassandra
Provides high availability with no single point of failure.
Aims to run on top of infrastructure of hundreds of nodes.
Manages persistent state even when components fail.

6. Dominant Approaches(Cont..)
Document Store :
MongoDB
Provides features like aggregation, ad hoc queries, indexing etc.
Stored in BSON format. Uses GridFS for storage.
Applications include CERN’s LHC, UIDAI Aadhar
Graph Databases :
Neo4j
Provides object oriented, flexible network structure.
Reliable, ACID compliant, highly available and scalable.
Used in software involving complex relationships like social networking.

7. Insights Gleaned Dynamo : Availability over Consistency
Feature for dynamic replication and accessing it.
Column indices to store data and usage of compression for efficient storage.
Operations and mechanism in Bigtable and Cassandra.
Use of document as a value and feature to have different internal structure.
Graph structure for storing information and concept of direct pointers.

8. Problem Space in the Future
90% of the data in the world today has been created in the last two years alone.
Data growth is being driven by unstructured data and billions of large objects.
Unstructured data leads to increased reliance on file storage pools, growing and increased storage administration.
Research may be put into optimized storage.
Companies will be looking to create custom data storage mechanisms.

9. Trade-off Space and Solutions in Future
Focus shift from retrieval time to storage
Object Storage seems to be the biggest base
BigData as a Service (BDaaS)
Diffcult to predict exact nature of data
Generic Data Stores for unstructured data
Data Store of Databases
Ultra Compression + Distributed Storage