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Logging

Make Your AWS Data Lake Deliver with ChaosSearch (Webinar Highlights)

When CTO James Dixon coined the term “data lake” in 2011, he imagined a single storage repository where organizations could store both structured and unstructured data in their raw format until it was needed for analytics. But without the right storage technology, data governance, or analytical tools, the first data lakes quickly became “data swamps” - morasses of data with no organizational structure and no efficient way to access or extract meaningful insights.

Feature Spotlight: Centralized Log Collection

Speedscale is proud to announce its Centralized Log Collection capability. When diagnosing the source of problems in your API, more information is better. For most engineers, the diagnosis process usually starts with the application logs. Unfortunately, logs are usually either discarded or stored in Observability systems that engineers don’t have direct access to. Compounding this issue is that the log information is typically not correlated to what calls were made against the API.

ChaosSearch in Two Minutes!

ChaosSearch helps modern organizations Know Better™ by activating the data lake for analytics. The ChaosSearch Data Lake Platform indexes customers’ cloud data, rendering it fully searchable and enabling analytics at scale with massive reductions of time, cost and complexity. ChaosSearch was purpose-built for cost-effective, highly scalable analytics encompassing full text search, SQL and machine learning capabilities in one unified offering. The patented ChaosSearch technology instantly transforms your cloud object storage (Amazon S3, Google Cloud Storage) into a hot, analytical data lake.

Unlocking Data Literacy Part 3: Choosing Data Analytics Technology

Ringing in the new year with new goals for data literacy? The right data management strategy can help democratize access to analytics across your entire team, without the need for a data scientist or data engineer to act as an intermediary or bottleneck. As you examine your people’s data skills and related data literacy training processes, it might be time to consider a new approach to data analytics technology that facilitates data democratization in 2022. That’s right, your platform.

Better Together with AWS - 2021 in Review

In 2021, many organizations on a digital transformation journey sought cloud-native data management and analytics solutions that could facilitate search and analytics on their data in the cloud. Many of them are already running on the AWS cloud, so naturally, they turned to the AWS Partner Network to find technologies that could easily plug into their cloud stack, solve tactical pains, and deliver value quickly.

Syslog Tutorial: Everything You Need to Know

Syslog is a protocol that allows you to transmit and receive notifications in a predefined format from various network devices. Timestamps, event messages, severity, host IP addresses, diagnostics, and other information are included in the messages. It may transmit a range of severity levels, including level 0, which is an emergency, level 5, which is a warning, System Unstable, critical, and levels 6 and 7, which are Informational and Debugging.

The Ultimate Guide to Logging in Python

Logging is used to track events that happen when an application runs. Logging calls are added to application code to record or log the events and errors that occur during program execution. In Python, the logging module is used to log such events and errors. An event can be described by a message and can optionally contain data specific to the event. Events also have a level or severity assigned by the developer. Logging is very useful for debugging and for tracking any required information.

Why Log Data Retention Windows Fail

If you’re using Elasticsearch as part of an ELK stack solution for log analytics, you’ll need to manage the size of your indexed log data to achieve the best performance. Elasticsearch indices have no limit to their size, but a larger index takes longer to query and is more costly to store. Performance degradation is often observed with large Elastic indices and queries on large indices can even crash Elasticsearch when they use up all of the available heap memory on the node.