dmagda commented on a change in pull request #6:
URL: https://github.com/apache/ignite-website/pull/6#discussion_r411562399
##########
File path: use-cases/in-memory-data-grid.html
##########
@@ -58,60 +57,60 @@ <h1><strong>In-Memory Data Grid</strong> with Apache
Ignite</h1>
<div class="container">
<p>
- Apache Ignite® is an in-memory data grid that accelerates and
scales your databases, services, and APIs.
- It supports key-value and ANSI SQL APIs, ACID transactions,
co-located compute, and machine learning
- libraries required for real-time applications.
- </p>
- <p>
- An in-memory data grid deployment is a read-through/write-through
caching strategy, in which the application
- layer treats the data grid as the primary data store. The
application layer writes to and reads from Ignite.
- Ignite ensures that any underlying database stays updated and
consistent with the in-memory data.
+ The Apache Ignite® in-memory data grid accelerates and scales your
databases, services,
+ and APIs. It supports key-value and ANSI SQL APIs, ACID
transactions, co-located processing,
+ and machine learning libraries. Ignite is frequently used to
increase the performance and
+ scalability of real-time applications, as a <a
href="/use-cases/digital-integration-hub.html">digital integration hub</a> to
provide real-time data
+ access to one or many applications to data from one or many siloed
data sources, for <a href="/use-cases/high-performance-computing.html">high
Review comment:
I do see that we want to get more searches of DIH and "cache" terms by
adding them to the first paragraph here but that's a tangential topic and we
shouldn't overwhelm our readers with more terms, especially, at the beginning
of the page.
I'll rework this paragraph after the merge.
##########
File path: use-cases/in-memory-data-grid.html
##########
@@ -58,60 +57,60 @@ <h1><strong>In-Memory Data Grid</strong> with Apache
Ignite</h1>
<div class="container">
<p>
- Apache Ignite® is an in-memory data grid that accelerates and
scales your databases, services, and APIs.
- It supports key-value and ANSI SQL APIs, ACID transactions,
co-located compute, and machine learning
- libraries required for real-time applications.
- </p>
- <p>
- An in-memory data grid deployment is a read-through/write-through
caching strategy, in which the application
- layer treats the data grid as the primary data store. The
application layer writes to and reads from Ignite.
- Ignite ensures that any underlying database stays updated and
consistent with the in-memory data.
+ The Apache Ignite® in-memory data grid accelerates and scales your
databases, services,
+ and APIs. It supports key-value and ANSI SQL APIs, ACID
transactions, co-located processing,
+ and machine learning libraries. Ignite is frequently used to
increase the performance and
+ scalability of real-time applications, as a <a
href="/use-cases/digital-integration-hub.html">digital integration hub</a> to
provide real-time data
+ access to one or many applications to data from one or many siloed
data sources, for <a href="/use-cases/high-performance-computing.html">high
+ performance computing</a>, or for data caching.
</p>
<img class="img-fluid diagram-right"
src="/images/svg-diagrams/data_grid.svg" alt="In-Memory Data Grid with Apache
Ignite"/>
- <p>
- As an in-memory data grid, Ignite provides all essential APIs
needed to simplify its adoption.
- The APIs include distributed key-value and ANSI SQL queries, ACID
transactions, co-located
- computations, and machine learning models. While key-value and SQL
calls let you request, join, and
- group distributed data sets, the compute and machine learning
components help to eliminate data
- shuffling over the network, thus, boosting compute and
data-intensive calculations.
- </p>
-
- <p>
- Ignite is capable of storing data both in memory and on disk with
two options for data persistence
- -- you can persist changes in an external database or let Ignite
keep data in its native persistence.
- Let's review both of these options.
- </p>
-
- <h2>Ignite and External Databases</h2>
-
- <p>
- Ignite can improve the performance and scalability of any external
database such as RDBMS,
- NoSQL or Hadoop, by sliding in as an in-memory cache between the
application and the database
- layer. When an application writes data to the cache, Ignite
automatically writes-through or
- writes-behind all data modifications to the underlying external
store. Ignite also performs
- ACID transactions where it coordinates and commits a transaction
across the cluster as well as
- the database.
- </p>
- <p>
- Additionally, Ignite can be deployed as a shared and unified
in-memory layer that stores data
- sets originating from disjointed databases. Your applications can
consume all the data from
- Ignite as a single store while Ignite can keep the original
databases in sync whenever in-memory
- data gets updated.
- </p>
- <p>
- However, there are some limitations if an external database is
used as a persistence layer for
- Ignite deployments. For instance, if you run Ignite SQL or scan
queries, you need to ensure that
- all the data has been preloaded to the in-memory cluster. Note
that Ignite SQL or scan queries
- can read data from disk only if it is stored in the native
persistence.
- </p>
-
- <h2>Ignite Native Persistence</h2>
- <p>Ignite native persistence is a distributed ACID and SQL-compliant
disk store that transparently integrates
- with Ignite in-memory layer. When the native persistence is
enabled, Ignite stores both data and indexes on
- disk and eliminates the time-consuming cache warm-up step. Since
the native persistence always keeps a full
- copy of data on disk, you are free to cache a subset of records in
memory. If a required data record is
- missing in memory, then Ignite reads it from the disk
automatically regardless of the API you use -- be it
- SQL, key-value, or scan queries.</p>
+
+ <h2>What is an In-Memory Data Grid?</h2>
+
+ <p>An in-memory data grid is a read-through/write-through caching
strategy in which the application
+ layer treats the data grid as the primary data store. The application
layer writes to and reads
+ from the in-memory data grid. The in-memory data grid ensures that any
underlying databases stay
+ updated and consistent with the in-memory data.</p>
+
+ <p>The Ignite in-memory data grid provides all essential APIs needed
to simplify its adoption. The
+ APIs include <a href="/use-cases/key-value-store.html">distributed
key-value</a> and <a href="/features/sql.html">ANSI SQL</a> queries, <a
href="/features/acid-transactions.html">ACID transactions</a>, <a
href="/features/collocated-processing.html">co-located processing</a>,
+ and <a href="/features/machinelearning.html">machine learning</a>
models. While key-value and SQL calls let you request, join, and group
+ distributed data sets, the compute and machine learning components
help to eliminate data
+ shuffling over the network, thus, boosting compute and data-intensive
calculations.</p>
+
+ <p>Ignite can store data both in memory and on disk with two options
for data persistence. You
+ can persist changes in an external database or have Ignite keep data
in its <a href="/arch/native-persistence.html">native persistence</a>. </p>
+
+ <h2>IGNITE in-memory data grid AND EXTERNAL DATABASES</h2>
Review comment:
We need to say "Ignite as an in-memory data grid ..." in headers to
avoid any confusion that Ignite has a dedicated feature-component named
similarly. "as an" highlights that it's one of the usage options. I'll update
after merging changes.
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