What is an Intelligent Data Platform?

Generative AI brings a life time oppurtunity for enterprises to significantly up their productivity game. However, Most enterprises are already plagued by fragmented data stack (with more than 8-10 point data solutions, with a man in the middle approach). Poor data outcomes despite serious infrastructure roll out coupled with prohibitive pricing models, have led to these investments becoming ROI un-friendly as the consumption rises.

Intelligent data platform are the next platform shift in the enterprises journey to benefit from Generative AI.

Besides unifying various point data solutions, Intelligent data platform integrates LLM Orchestration and LLM stack right within the unified data platform. This besides helping unify various workloads i.e Data, ML and AI and various data personas including Data Analyst, Data Engineer, ML Engineer and Data Scientists, but also helps leverage the same LLM infrastructure for the control plane of the data platform itself.

What is Xceed Analytics ?

Xceed Analytics is one of the earliest and one of the most promising Intelligent data and AI platform available today. It empowers enterprises to acheive all the data, analytics and AI use-cases across enterprise. By unifying all the enterprise data, analytics, ML and AI use cases, A platform like Xceed Analytics helps significantly accelarate the use-case development, while managing complexity of integrating, monitoring and trouble-shooting various issues.

Advantages of Xceed Analytics

Following are some of the advantages of Xceed Analytics - An Intelligent Data Platform

Zero integration effort

Xceed Analytics control plane is built grounds up, seamlessly integrating a disparate set of data applications into a unified interface for an unparalled user experience. This tools includes the following:

1. Data Connectors
2. Data Catalog with AI Completion
3. SQL Query Editor with NL-SQL Co-pilot
4. Multi-engine cloud orchesrator with support for many OSS engines such as Apache Spark, Pandas, Duckdb.
5. No Code Workflow Designer, Classical ML via AutoML
6. AI/ML Model Catalog - Enables versioning and deploying various models built using Xceed Analytics in staging and production.
7. Import OSS LLM models
8. Playground: enables unit-testing various models and comparing between versions
9. Dashobaords and Workspaces: Build feature rich workspaces/dashboards and share with end-user.

Governance First

Unified Governance helps significantly reduce complexity of streamlining security, privacy and compliance processes by centralizing all the privacy and security needs under one expierence.

Effective Collaboration

Removes tools/user silos thereby improves collaboration between various data products roles and business users.

Focus on Business Value

Most importantly help enterprises accrue the benefits of their most important asset, i.e data instantly. Whether it's Business Intelligence or Data science or Analytics, enterprises are struggling to accrue the benefits of their most valuable data asset. Worst in many cases it's turning out to be a liability with large overheads of managing fragmented solution space and complexity that comes along with it, least to mention issues with governance and data security

Reliable Data and AI Products

Non Functional Requirements such as trust-worthiness, reliability are key to building any data product. Unified platform with right features can not just help build reliable data products faster but also significantly accelarate building data products themselves.

Engine Independence

Ability to use a varity of compute or relational engines or even AI models enables enterprises to benefit from OSS, select the engine that is fit for the use-case thereby making the right choice for a given use-case and optimize on cloud cost.

Storage and Engine Separation

Open Storage Architecture and Standard storage formats ensure that enterprises have access to their data as required without any vendor lock.

End-to-End Data ML Model Development

Xceed Analytics supports all the pieces of the puzzle for end-to-end needs for ML use-cases. From development of models, to versioning of models, to deployment of models via zero devops interfaces. All model artificats are available from the model repository page.

SDK Layer

Xceed analytics provides a unified sdk layer for most common relational operators both SQL and Dataframe. This is crucial to avoid having to learn newer language syntax and programming paradigm. Its multi-engine abstraction lets data users benefit from multiple execution environments, one that suites the requirements for a given data product or use-case. Currently supported engines for SQL User include: duckdb, apache drill, apache spark(open-source). For data users using python and dataframe interface, it supports pandas, ray and apache spark. This is a significant advantage that Xceed Analytics offers compared to other platforms for all kinds of sql, data engineering and data science workloads.

User Access Layer

Both nocode and code access layer interface brings together all the usecases and all the data roles together to bring value faster. IT doesn't need to pick the tool based on interface that a user role expects. Unified Analytics Platform must provide common interfaces to meet the needs of various data producer needs. Be it API, SDK, No Code, SQL, Semi-declarative(yaml) or even natural language as we get into the future.

Role Based Experiences

Unified Data Platform such as Xceed further supports the following key services/features for each of the data roles. Quick Summary of the features for each roles is as below.

Services	Description	Role(s)
Data Connectors	Connect your datasources and load the same in Xceed Data Lake	DE
Data Catalog	Explore, Govern and Manage all your data from data catalog	All Roles
Data Preparation	Transform, Cleanup, Engineer Features	DE, DS
SQL Query	Query data and create views using SQL	BA, DA, DS, DE
Dashboards	Create reports, dashboards with stories and share insights	BA, DS
ML Experiments	Create and prototype ML Experiments quickly	DS, MLE
ML Catalog	Manage, Deploy all the published models, Track Revisions	DS, MLE
Command Line Interface	Configure, Manage Xceed Analytics via CLI	DE, DS, MLE, Administrator
Python SDK	Configure, Manage Xceed Analytics via SDK	DE, DS, MLE, Administrator

Acronyms for Roles

Acronym	Description
DE	Data Engineer
DS	Data Scientist
MLE	ML Engineer
BA	Business Analyst
DA	Data Analyst
BU	Business User
AI	Artificial Intelligence
LLM	Large Language model