01 — Elastic resource scaling

Demand rises.
Capacity follows.

Cloud platforms add CPU, memory, and servers during heavy analytics—then scale back when the workload falls.

Fast performance · controlled cost

Live workload / automatic mode

02active compute nodes

Assign analytics workload

18% Current workload

12%Each compute node handles up to 12% of the assigned workload.

Auto demoDrag to configure

Node 01Idle

Node 02Idle

Node 03Idle

Node 04Idle

Node 05Idle

Node 06Idle

Node 07Idle

Node 08Idle

Node 09Idle

Capacity matched

02 — Distributed processing

Split the data.
Solve it together.

Hadoop and Apache Spark divide massive datasets into smaller parts, processing them at the same time across many machines.

Parallel work · dramatically less time

Parallel compute / automatic mode

1×estimated speedup

8 TBRaw dataset

NODE 01

NODE 02

NODE 03

NODE 04

NODE 05

NODE 06

✓Unified result

Parallel workers01

Sequential96 min

Parallel96 min

Single worker baseline

03 — High-availability storage

One fails.
Your data doesn’t.

Cloud storage replicates data across servers and locations, so another copy remains available if a server or data centre goes down.

Reliable · fault-tolerant · always accessible

Failure simulator / automatic mode

3 / 3healthy data copies

Primary copy

Replica copy

Traffic rerouted ↗

Replication factor3 copies

Single copyAt risk

ReplicatedProtected

Three copies synchronized

Demand rises.Capacity follows.

Demand rises.
Capacity follows.