1.
Distributed Computing
Abstractions for Big Data
Cloud Analytics
Dr. Vijay Srinivas Agneeswaran
Director of Technology,
Data Sciences Group,
SapientNitro,
Bangalore.

2.
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Big Data Computations
Computations/Operations
Giant 1 (simple stats) is perfect for
Hadoop 1.0.
Giants 2 (linear algebra), 3 (N-
body), 4 (optimization) Spark from
UC Berkeley is efficient.
Logistic regression, kernel SVMs,
conjugate gradient descent,
collaborative filtering, Gibbs
sampling, alternating least squares.
Example is social group-first
approach for consumer churn
analysis [2]
Interactive/On-the-fly data
processing – Storm.
OLAP – data cube operations.
Dremel/Drill
Data sets – not embarrassingly
parallel?
Deep Learning
Artificial Neural Networks/Deep
Belief Networks
Machine vision from Google [3]
Speech analysis from Microsoft
Giant 5 – Graph processing –
GraphLab, Pregel, Giraph
[1] National Research Council. Frontiers in Massive Data Analysis . Washington, DC: The National Academies Press, 2013.
[2] Richter, Yossi ; Yom-Tov, Elad ; Slonim, Noam: Predicting Customer Churn in Mobile Networks through Analysis of Social Groups.
In: Proceedings of SIAM International Conference on Data Mining, 2010, S. 732-741
[3] Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew W.
Senior, Paul A. Tucker, Ke Yang, Andrew Y. Ng: Large Scale Distributed Deep Networks. NIPS 2012: 1232-1240

3.
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3
What is Hadoop?
This diagram is from
http://2.bp.blogspot.com/_mbMKzl2KhSc/TOOF6OQp5fI/AAAAAAAABb8/l2gyhVhQw9Y/s1600/D-Hadoop+MapReduce+Architecture.png

4.
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4
Data Flow in Spark and
Hadoop

5.
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Comparison of Graph Processing Paradigms
Graph
Paradigm
Computation
(Synchronous or
Asynchronous)
Determinism
and Effects
Efficiency VS
Asynchrony
Efficiency of
Processing Power
Law Graphs.
GraphLab1 Asynchronous Deterministic –
serializable
computation.
Uses inefficient
locking protocols
Inefficient – Locking
protocol is unfair to
high degree vertices
Pregel Synchronous – BSP
based
Deterministic –
serial
computation.
NA Inefficient – curse of
slow jobs.
Piccolo Asynchronous Non-
deterministic –
non-serializable
computation.
Efficient, but
may lead to
incorrectness.
May be efficient.
GraphLab2
(PowerGraph)
Asynchronous Deterministic –
serializable
computation.
Uses parallel
locking for
efficient,
serializable and
asynchronous
computations.
Efficient – parallel
locking and other
optimizations for
processing natural
graphs.

6.
GraphLab: Ideal Engine for Processing Graphs [YL12] – Dato.
[YL12] Yucheng Low, Danny Bickson, Joseph Gonzalez, Carlos Guestrin, Aapo Kyrola, and Joseph M. Hellerstein. 2012. Distributed GraphLab: a
framework for machine learning and data mining in the cloud. Proceedings of the VLDB Endowment 5, 8 (April 2012), 716-727.
Goals – targeted at
machine learning.
• Model graph dependencies,
be asynchronous, iterative,
dynamic.
Data associated with edges (weights, for
instance) and vertices (user profile data,
current interests etc.).
Update functions – lives on each
vertex
• Transforms data in scope of vertex.
• Can choose to trigger neighbours (for
example only if Rank changes drastically)
• Run asynchronously till convergence – no
global barrier.
Consistency is important in ML
algorithms (some do not even converge
when there are inconsistent updates –
collaborative filtering).
• GraphLab – provides varying level of
consistency. Parallelism VS consistency.
Implemented several algorithms,
including ALS, K-means, SVM, Belief
propagation, matrix factorization, Gibbs
sampling, SVD, CoEM etc.
• Co-EM (Expectation Maximization) algorithm
15x faster than Hadoop MR – on distributed
GraphLab, only 0.3% of Hadoop execution time.

7.
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Other Abstractions
• Functional data science
• Exploration of recent algorithms
• EMPCA (Expectation-Maximization Principal component analysis)
• ADMM (Alternating Direction Method of Multipliers)
• Deep Learning
• Distributed computing abstractions for data science
- Useful for engineering RFPs and next gen architectures for clients.
• Iterative computations – Apache Spark or Flink or MPI or Concord
• Graph computations – TensorFlow, GraphLab, Titan.
• High performance computations – CUDA.

8.
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Other Abstractions
• Adhoc queries on large data sets.
– Next gen SQL
• Approximate queries – Blink DB
• Adhoc queries – Apache Drill
• Distributed SQL – Spark SQL, Apache Tajo, HAWQ
• Data governance
– Important for all big data architectures.
• Security – policies for role based fine grained access control
• Encryption – at-rest data as well as data-in-motion.
• Tokenization/Obfuscation – data masking.

9.
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Future Abstractions
• Internet of Things (IoT) Analytics
• Fog/Edge computing – strong devices, weakly connected.
• Data Lake Management
– next gen data management
• Hadoop based data lakes
• NewSQL data stores

10.
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Thank You!
Mail • vagneeswaran@sapient.com
LinkedIn • http://in.linkedin.com/in/vijaysrinivasagneeswaran
Blogs • blogs.impetus.com
Twitter • @a_vijaysrinivas.