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DOCS-822: minor text tweaks and backport (#1119)
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* DOCS-708: remove jet-start.sh links

* DOCS-822: rename overview page and add description

* DOCS-822: text tweak and prep for backporting

* Update docs/modules/ROOT/pages/index.adoc

Co-authored-by: rebekah-lawrence <[email protected]>

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Co-authored-by: rebekah-lawrence <[email protected]>
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oliverhowell and rebekah-lawrence authored May 22, 2024
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8 changes: 3 additions & 5 deletions docs/modules/ROOT/pages/index.adoc
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:!page-pagination:
:description: Hazelcast Platform uniquely combines a distributed compute engine and a fast data store in one runtime. It offers unmatched performance, resilience and scale for real-time and AI-driven applications.

{description}

Hazelcast is a distributed computation and storage platform for consistently
low-latency querying, aggregation and stateful computation against event
streams and traditional data sources. It allows you to quickly build
streams and traditional data sources. {description} It allows you to quickly build
resource-efficient, real-time applications. You can deploy it at any scale
from small edge devices to a large cluster of cloud instances.

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processes for coordination and high availability. With Hazelcast, when you start
another process to add more capacity, data and backups are automatically and evenly balanced.

NOTE: “Node” and “Member” are interchangeable, and both mean a Java Virtual Machine (“JVM”) on which one or more instances of Hazelcast software are in operation.

== What Can You Do with Hazelcast?

You can request data, listen to events, submit data processing tasks using
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depending on the memory capacity of your system. Hazelcast also automatically creates backups of these partitions
which are also distributed in the cluster. This makes Hazelcast resilient to data loss.

NOTE: _Node_ and _Member_ are interchangeable, and both mean a Java Virtual Machine (JVM) on which one or more instances of Hazelcast are in operation.

Hazelcast's *streaming engine* focuses on data transformation while it does all the heavy
lifting of getting the data flowing and computation running across a cluster of members.
It supports working with both bounded (batch) and unbounded (streaming) data.
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