Tachyon

The simplest, quickest and cheapest way to track, collect, clean and democratise your big data. Tachyon is ideal product to pilot any machine learning project for your business, with the scalability to promote to full production at any time.

  • Data Validation

    Verify and validate your data on ingestion.

  • Data Processing

    Use a number of supported industry standard integrated tools

  • Build Schema & Pipelines

    Schema driven engine with multi-step pipelines

  • Collect Any Data

    Retrieve or push any data simply and efficiently

  • Total Control & Security

    Multi-tenanted system with version control and complete security for piece of mind

Data Ingestion

Your path through a digital transformation doesn’t have to be complex, long and costly one.

Tachyon is a simple, highly scalable, ultra performant big data collection, consolidation and democratisation platform.

To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security.

what is data ingestion?

Data ingestion is the transportation of data from assorted sources to a storage medium where it can be accessed, used, and analyzed by an organization. The destination is typically a data warehouse, data mart, database, or a document store.

Data Vault

Data Vault is a method and architecture for delivering a Data Analytics Service to an enterprise supporting its Business Intelligence, Data Warehousing, Analytics and Data Science requirements. At the core it is a modern, agile way of designing and building efficient, effective Data Warehouses.

Tachyon is a friend of BI Developers, Analysts and Data Scientists

This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.

NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.

Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.

Performance Scalabilty

The Most performant & scalable big data pipeline in the market
Performance need not come at a high cost, Tachyon has been engineered from the ground up with an unwaivering commitment to maintaining extreme performance at all costs. This translates into cost savings for our clients and low latency data processing.

The elements of AIOps

Gartner explains how an AIOps platform works by using the diagram in Figure 1. AIOps has two main components: big data and ML. It requires a move away from siloed IT data in order to aggregate observational data (such as that found in monitoring systems and job logs) alongside engagement data (usually found in ticket, incident, and event recording) inside a big data platform.

This is the evolution of the traditional ITOM domain that integrates development (traces) and other non-ITOM data (topology, business metrics) to enable new modalities of correlation and contextualization. In combination with real-time processing, probable-cause identification becomes simultaneous with issue generation.

  • Extensive and diverse IT data: Enumerated in the black and blue chevrons, AIOps is predicated on bringing together diverse data from both IT operations management (ITOM) (metrics, events, etc.) and IT service management (ITSM) (incidents, changes, etc.). This is often referred to as “breaking down data silos”—bringing data together from disparate tools so they can “speak” to each other and accelerate root cause identification or enable automation.
  • Machine learning: Big data enables the application of ML to analyze vast quantities of diverse data. This is not possible prior to bringing the data together nor by manual human effort. ML automates existing, manual analytics and enables new analytics on new data—all at a scale and speed unavailable without AIOps.
  • Engage: The evolution of the traditional ITSM domain includes bi-directional communication with ITOM data to support the above analyses and auto-create documentation for audit and compliance/regulatory requirements. AI/ML expresses itself here in cognitive classification plus routing and intelligence at the user touchpoint, e.g., chatbots.
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