Big Data Platforms, Tools and Techniques

- Overview

Big data tools and techniques demand special big data tools and techniques. When it comes to managing large quantities of data and performing complex operations on that massive data, big data tools and techniques must be used. The big data ecosystem and its sphere are what we refer to when we say using big data tools and techniques. 

There is no solution that is provided for every use case and that requires and has to be created and made in an effective manner according to company demands. A big data solution must be developed and maintained in accordance with company demands so that it meets the needs of the company. A stable big data solution can be constructed and maintained in such a way that it can be used for the requested problem.

- Big Data Platforms

The constant flow of information from various sources is becoming denser, especially as technology advances. This is where big data platforms are used to store and analyze ever-increasing amounts of information.

A big data platform is a comprehensive computing solution that combines many software systems, tools, and hardware for big data management. It is a one-stop architecture that addresses all data needs of an enterprise regardless of the volume and size of the data at hand. Due to the high efficiency of data management, enterprises are increasingly adopting big data platforms to collect and transform large amounts of data into structured, actionable business insights.

Currently, the market is flooded with numerous open source and commercial big data platforms. They have different features and capabilities to be used in a big data environment.

Choosing the right big data platform depends on various factors such as the size and complexity of the data, processing and analysis requirements, and of course budget.

- Characteristics of A Big Data Platform

Any good big data platform should have the following important characteristics:

• Ability to adapt new applications and tools based on changing business needs
• Support multiple data formats
• Capable of holding large amounts of streaming or static data
• Have a variety of conversion tools to convert data into different preferred formats
• Ability to accommodate data at any speed
• Provide tools to search data through massive datasets
• Support linear scaling
• Rapid Deployment Capability
• Have tools for data analysis and reporting requirements

- Incident Management for High-Velocity Team

In today's always-on world, disruptions and technology events are more important than ever. Failures and downtime have real consequences. Missed deadlines. Delayed payment. Engineering delays. That's why it's imperative for companies to quantify and track metrics about uptime, downtime, and the speed and efficiency with which teams resolve issues.

Some of the metrics most commonly tracked by the industry are "Mean Time to Detect or Discover (MTTD)", "Mean Time to Restore (MTTR)" - a family of metrics designed to Helping technical teams understand how often incidents occur and how quickly teams can recover from them.

Many experts argue that these metrics are actually not that useful on their own because they don't ask more complex questions about how incidents were resolved, what worked and what didn't, and how, when and why problems escalated, or de-escalated.On the other hand, MTTR, MTBF, and MTTF can be a good benchmark or baseline to start a conversation that leads to deeper, more important questions.

• "Mean Time to Detect or Discover (MTTD)" is a measure of how long a problem exists in an IT deployment before the appropriate parties become aware of it. MTTD is a key performance indicator (KPI) for IT incident management. A shorter MTTD indicates that users suffer from IT disruptions for less time than with a longer MTTD. MTTD may also be referred to as mean time to identify (MTTI).
• "Mean Time to Restore (MTTR)" is the average amount of time it takes to recover from an incident. An incident is an event that leads to an interruption of normal operations. In other words, they are anything that cause downtime, like bugs or external system outages. Mean Time to Restore is a key metric in any incident management system, since it captures the severity of the impact - that is, how long the application was down for.