Data Science, Artificial Intelligence, Analytics, and Machine Learning at the Enterprise scale are terms you’ve probably heard before. But what do they mean? We break it down for you in this blog.
So, What Is Data Science?
Data Science is a series of disciplines, technology, skills, expertise, and knowledge that encompass one thing: obtaining and preparing data for analysis. In parallel to this, we have to quantify, originate, and structure the data to make it useful with analytics by securing, cleaning, aggregating, and manipulating the data for the consumption of analytical routines, solvers, packages, models, data warehouses, and data archives. The main objective of Data Science is to capture data.
In this blog, we will explore the future of Data Science at the enterprise scale from an analytical, computational data architectural future viewpoint. We will look at how Qubole is making this future possible.
Data Science is a critical component of success for any company in today’s competitive environment. Qubole’s mission is to build the only Autonomous Data Platform, allowing data scientists to focus on what matters, and solve the most pressing big data challenges for their business.
Let’s understand what Machine Learning (ML) and/or Artificial Intelligence (AI) are.
Machine Learning is a series of intelligent codes, an automated organized set of solvers, algorithms, rules, and routines that consume/create data.
The sole purpose of machine learning is to report on the past and show, learn, determine, capture, and interact with the present. This enables you to make decisions and predict and simulate the future, in a self-aware, self-learning adaptive type of runtime computational model. We are only as smart as our data since data is everything. Enterprise scale is the magnitude of your intelligence or data.
Some of the key elements are:
- Compute: utility in nature
- Storage: pervasive and intelligent
- Performance: real-time, acid / smart data
- Scalability: compute, storage, connectivity
- Self-Intelligence: Machine Learning
- Security: double tunnel walled gardens
What Is A Cloud?
Cloud is a very common word that I am sure you hear often in your daily life. The cloud refers to servers that are accessed over the internet and the software and databases that run on those servers. Cloud servers are located in data centers all over the world. By using cloud computing, users and companies do not have to manage physical servers themselves or run software applications on their own machines. The cloud enables users to access the same files and applications from almost any device. Cloud computing is nothing but the evolution of unpromised computing over the last 50 years and connecting them with fiber optics. Whether we talk about the raw data, called data lake or data warehouse, the data is on the cloud. The storage mechanism of data is utilized for just one thing: analytics. Data is evolving to be self-contained, executable intelligent data sets, as a strategic asset and Intellectual Property (IP).
Future Use Cases Of Artificial Intelligence/Machine Learning:
- Analytics from simple to deep, scientific to self-aware
- Mergers and acquisitions
- Different data types
- Data growth, ingesting, Internet of Things (IoT), blockchain
- Research and development
- Training and simulations
- Backups and data protection
Use Cases For Data Lake(S) For Machine Learning & Artificial Intelligence Across Industries And Sectors:
- Financial sector: credit unions
- Pharmaceuticals: pacemakers, medical devices
- Life Sciences: genomic sequencing
- Government: security and surveillance
- Entertainment: Gaming
All Data-Driven Organizations Use Data In Three Ways:
- Learn from the past: This involves looking at the historical data for past learnings and experiences
- Understand the present: Using data exploration and analytics to understand the real-time and streaming data
- Predict the future: Using the past and current data to predict the future with AI/ML
What Is An Open Data Lake?
An Open Data Lake ingests data from sources such as applications, databases, data warehouses, and real-time streams. It formats and stores the data into an open data format that is platform-independent, machine-readable, optimized for fast access and analytics, and made available to consumers without restrictions that would impede the re-use of that information.
So, Why The Qubole Data Platform?
Qubole is a simple, open, and secure Data Lake Platform for machine learning, streaming, and ad-hoc analytics. Our platform provides end-to-end services that reduce the time and effort required to run Data Pipelines, Streaming Analytics, and Machine Learning workloads on any cloud. Some of the reasons to choose Qubole are:
- Simple, open, and secure platform
- Fast adoption of data lakes
- Near-zero administration
- Reduced data lake cost by more than 50%
No other platform radically simplifies data management, data engineering, and run-time services like Qubole. Qubole enables reliable, secure data access and collaboration among users while reducing time to value, improving productivity, and lowering cloud data lake costs from day one.
Qubole provides data science teams with the best tool for every task in the data science life cycle- in a single, cloud-native platform. With Qubole, data scientists no longer need to rely on data admins for provisioning compute clusters and resources. Once the exploratory data analysis and/or model development is done, data scientists can productize their notebooks using the notebook API with just a few clicks.
Data Fabric provides a tooling ecosystem to optimize the functions of data architecture, data governance, and data analytics. The goal of Data Fabric is to create an architecture that encompasses all forms of analytical data for any type of analysis with seamless accessibility and shareability for all those with a need for it. Qubole contributes to the Data Fabric in terms of data democratization, data integration, and data governance bolstered by Advanced Analytics and Data Science, enabling the consumer to leverage the insights in a cost-effective manner.
Qubole’s integrated User Interface lets you access, explore, and visualize data all in one place. You have a central location to access data, along with metadata. Additionally, Qubole offers offline metadata access, and Notebooks can save results offline. This means you can access, review, and inspect Notebooks offline, more conveniently and at a lower cost since you do not need to start a cluster each time.
