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19th April30 min. Product Demo

14:00AM New York Time

11:00AM London Time

WHY SYSLOG-NG

Collect From a Wide Variety of Sources

syslog-ng can collect logs from legacy systems, web servers, SQL databases as well as any applications or devices generating JSON messages or text-based files.

Flexibly Route Data

Data consumers have different needs and many organizations use a variety of data management and analysis tools, syslog-ng can flexibly route data from X sources to Y destinations.

BENEFITS

Managing the multitude of log types across all of your systems and applications is only part of the problem, putting the data into your data lake also poses problems.

From this 30-minutes webinar session you will learn how syslog-ng users manage their logs, normalize, and ingest the data into their big data applications.

Transform data in real-time

Using syslog-ng’s Pattern Database you can classify, filter, parse, and re-write to transform disparate data types in real-time. Take text data and create key-value pairs for a variety data stores.

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Feeding log data into your Data Lake

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Reduced deployment and maintenance costs

syslog-ng’s architecture enables you to deploy the same software packages on a more than 50 different server platforms and manage.

Confidence in your data

Getting all of your data means you can rely on the insights derived from your analytic platform.

Data when you need it

Having data delivered in real-time means you can react and get answers faster.

Secure data

Data is valuable and end to end encryption with syslog-ng Premium Edition prevents unwanted 3rd party access.

Deliver Data with End-to-End Reliability

Data transfer over TCP and the Reliable Log Transfer Protocol (RLTP),local disk buffering, client-side failover and other features ensure zero message loss, giving you confidence in your data.

CHALLANGES

Wide Variety of Data

Delivering data in disparate formats from systems, applications, and devices often requires multiple tools and special integration.

Massive data volumes

Big data is, well, big. Many data sources can overwhelm data collection tools.

Difficult to access data

Most big data systems capture data from complex, distributed systems, often from multiple remote sites with a variety of connectivity and latency issues.

Incomplete data

Insights based on incomplete data are often wrong. In large environments, it’s easy to leak data during collection and ingestion.