Verify that your environment meets the system requirements for Sensitive Data Governance.
Commvault Packages
To use Activate for Sensitive Data Governance, install the following packages on computer(s). You will use the computer with the packages to create an Index Server in the Sensitive Data Governance guided setup.
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Index Store
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Content Analyzer
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Web Server
Operating System
The computer that you install the required Commvault packages on must use one of the following operating systems.
Windows
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Microsoft Windows Server 2022 Editions
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Microsoft Windows Server 2019 Editions
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Microsoft Windows Server 2016 x64 Editions
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Microsoft Windows Server 2012 R2 x64 Editions
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Microsoft Windows Server 2012 x64 Edition
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Red Hat Enterprise Linux/CentOS
Linux
- Red Hat Enterprise Linux/CentOS 7.3 and above
Hardware Specifications for the Index Server
Use the following guidelines to select the appropriate hardware for your Index Server.
Considerations
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Size up to the larger specification if either of the following cases applies to you:
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Your environment is between two sizes
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You are using one Index Server for both Exchange data sources and Exchange backups
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If you are using one Index Server for both files and email messages, use the size that matches the larger source. For example, if you have 40 TB of file source data and 15 TB of email source data, use the specification for a medium-sized server.
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The following options affect the performance of the Index Server:
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Custom entities that include multiple regular expressions
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Data classification plans that include the optical character recognition (OCR) option or the pre-generated previews option
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Specifications for File Data and Email Messages
|
Component |
Large |
Medium |
Small |
|---|---|---|---|
|
File source data size per node (based on an average file size of 1 MB and on the assumption that 50 percent of documents are eligible for content indexing and there is only one version of the file) |
160 TB |
80 TB |
40 TB |
|
Email source application size |
25 TB |
15 TB |
5 TB |
|
File objects per node (estimated) |
80 million |
40 million |
20 million |
|
Email objects per node (an estimate based on an average message size of 100 KB. Messages with attachments are considered a single object) |
250 million |
150 million |
50 million |
|
CPU |
32 cores |
16 cores |
8 cores |
|
RAM |
64 GB |
32 GB |
16 GB |
|
Index disk space (SSD class disk recommended) |
12 TB |
6 TB |
3 TB |
Specifications for Dedicated Servers for File Data
|
Component |
Large |
Medium |
Small |
|---|---|---|---|
|
Source data size per node (based on an average file size of 1 MB and on the assumption that 50 percent of documents are eligible for content indexing and there is only one version of the file) |
160 TB |
80 TB |
40 TB |
|
Objects per node (estimated) |
80 million |
40 million |
20 million |
|
CPU or vCPU |
32 cores |
16 cores |
8 cores |
|
RAM |
64 GB |
32 GB |
16 GB |
|
Index disk space (SSD class disk recommended) |
12 TB |
6 TB |
3 TB |
Specifications for Dedicated Servers for Email Messages
|
Component |
Large |
Medium |
Small |
|---|---|---|---|
|
Source application size |
25 TB |
15 TB |
5 TB |
|
Objects per node (an estimate based on an average message size of 100 KB. Messages with attachments are considered a single object) |
250 million |
150 million |
50 million |
|
Number of mailboxes (based on an average mailbox size of 5 GB, and an average of 50,000 messages per mailbox) |
5000 |
2000 |
400 |
|
CPU |
16 cores |
16 cores |
8 cores |
|
RAM |
64 GB |
32 GB |
16 GB |
|
Index disk space (SSD class disk recommended) |
10 TB |
6 TB |
2 TB |
Hardware Specifications for the Content Analyzer
Content analyzer computers detect personally identifiable information (PII) in the data. Some Activate environments require dedicated content analyzer computers.
Review the following use cases to determine if you need dedicated content analyzer computers:
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You need to identify machine learning-based entities. For example, Address, Contextual Date, Location, Money, Organization, Person, and Time are entities that are identified by using machine learning. For a complete list of built-in entities, see Personally Identifiable Information.
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You want to configure parallel processing. You can add multiple content analyzer computers, and then create a separate data classification plan for each content analyzer.
Before you install the Content Analyzer package, use the following guidelines to select the appropriate hardware for your content analyzer computers.
|
Use case |
CPU |
RAM |
Disk space |
|---|---|---|---|
|
Machine learning-based entities |
32 cores |
64 GB |
2 TB |
|
Parallel processing |
16 cores |
32 GB |
1 TB |
Tip
To distribute the processing load, you can install the Content Analyzer package on multiple computers.