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Securely Networking Big Data
Environments
Stephen Hampton
CTO, Hutchinson Networks
Big Data On The Network – An Example
• HDFS (Hadoop Distributed File System)
• Name Node (HDFS Brain, List of Blocks & Met...
Big Data Network Characteristics
• Availability – The loss of a portion of the cluster will impact performance.
• Burst Ha...
Big Data On Traditional Networks
• Availability – Resilient but slow to converge and prone to
Layer 2 problems (Loops and ...
Big Data On Network Fabrics
• Availability – ECMP with sub-second failover and
flow-lets.
• Burst Handling – Low over-subs...
What Is Networking Your Big Data?
Whether it’s on premise in your own data centre, co-location or cloud, check that your
n...
What About Big Data for Networking?
• SIEM
• Infrastructure Analytics
• Monitoring & Troubleshooting
• Intent Based Automa...
Securely Networking Big Data
Environments
Stephen Hampton
CTO, Hutchinson Networks
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Secure Networking in Big Data Environments

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A presentation by Stephen Hampton at The Cyber Academy

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Secure Networking in Big Data Environments

  1. 1. Securely Networking Big Data Environments Stephen Hampton CTO, Hutchinson Networks
  2. 2. Big Data On The Network – An Example • HDFS (Hadoop Distributed File System) • Name Node (HDFS Brain, List of Blocks & Meta Data) • Data Node (Independent Nodes, Capable of Executing Workloads) • Process • Write Data (Data Blocks Uploaded) • Workload Execution • Map Phase (Little or no data) • Shuffle Phase (Data on the network) • Reduce Phase (Little or no data) • Output Replication (Data on the network) • Reading Data (Data Read By Application)
  3. 3. Big Data Network Characteristics • Availability – The loss of a portion of the cluster will impact performance. • Burst Handling – Queue depth and low over-subscription ratio are very important. • Latency – There is delay in the processing on the data node, so this is not an issue. • Jitter (Variation in Delay) – Some workloads are highly synchronous, so deterministic latency is important. • Scale – The environment should easily scale and retract to fit requirements. • Security & Multi-Tenancy – A single environment for multiple logical work loads is more efficient. • Performant – Server 10GE connectivity is common-place and likely to increase to 25 GE, 40 GE and 50 GE.
  4. 4. Big Data On Traditional Networks • Availability – Resilient but slow to converge and prone to Layer 2 problems (Loops and Broadcast Storms). • Burst Handling – Queue depths are good but over- subscription ratios can be high. • Latency/Jitter – Over-subscription can lead with jitter. • Scale – Difficult to scale without manual configuration • Security – Firewall is a bottleneck and DMZs are difficult to design. • Performant – High speeds available but these are not the most efficient or cost effective. Core Layer Distribution Layer Access Layer Big Data Cluster Nodes 1 Gbps1 Gbps 10 Gbps 1 Gbps1 Gbps Firewall Layer
  5. 5. Big Data On Network Fabrics • Availability – ECMP with sub-second failover and flow-lets. • Burst Handling – Low over-subscription ratios and optimised use of bandwidth. • Latency/Jitter – Predictable latency throughout. • Scale – Very easy to scale, add more leafs and deploy via controller. • Security – Secure micro-segmentation, DMZs everywhere. • Performant – Multiple 40/100 GE ports in fabric with 1/10 GE to servers. Big Data Cluster Nodes 1/10 Gbps 40 Gbps Ethernet Fabric
  6. 6. What Is Networking Your Big Data? Whether it’s on premise in your own data centre, co-location or cloud, check that your network is… • An Network Fabric • Supports ECMP (Equal Cost Multi-Pathing) • Provide 40 GE (future 100 GE Support), along with 10GE edge support • Is built on Software Defined Network • Uses Network Functions Virtualisation for security • Implements secure micro-segmentation
  7. 7. What About Big Data for Networking? • SIEM • Infrastructure Analytics • Monitoring & Troubleshooting • Intent Based Automation
  8. 8. Securely Networking Big Data Environments Stephen Hampton CTO, Hutchinson Networks

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