Name: What is Qubole?
Uploaded: 2019-11-19T23:24:18.000Z
Duration: 1 min 2 s
Description: Overview of Qubole's Data Platform for Self-Service AI, Machine Learning, and Analytics

Help Desk

Web and Video Conferencing

Customer Support

Batch Management Software

Feature Management

Innovation Management

Product Lifecycle Management (PLM)

Development

Classroom Management

Education

Business Intelligence (BI)

Calendar

Collaboration

Digital Rights Management (DRM)

Enterprise Content Management

Statistical Analysis

Visitor Management

Enterprise

Accounting

Corporate Performance Management (CPM)

Digital Banking

Finance and Accounting

ACA Compliance

Applicant Tracking

Corporate Learning Management

HR Management

Leadership Development

Payroll

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

Access Gateways

Archiving

Cloud Storage

Configuration Management Databases

Disk Cloning

Email Encryption

Fraud Detection

Infrastructure-as-a-Service

Integration Platform as a Service (iPaaS)

Log Management

Network Performance Monitoring

Office Migration and Repair

SIP Trunking

Software Asset Management

Telecom Expense Management (TEM)

Vendor Risk Management

Virtual Data Room

Web Scraping

WiFi Hotspot

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Blogging

Content Management

Ecommerce Personalization

Email Marketing

Enterprise Marketing Management

Market Intelligence

Market Research

Marketing Automation

Social Media Management

Survey & Forms Building

Video Marketing

Web Analytics

Marketing

Job Site Management

Project Management

Waste Management

Writing and Proofreading Tools

Professional Services

Customer Onboarding

Customer Relationship Management (CRM)

Sales Performance Management

Sales

Fleet Management

Membership Management

Municipal

Rental Property Management

Roofing

Warranty Management

Vertical-Specific

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### Big Data Management
Users generally appreciate Qubole's efficient handling of big data and its advanced analytics capabilities. The platform's ability to manage massive datasets and streamline data processing tasks has garnered positive feedback. Users find the platform user-friendly and efficient for running complex queries on various big data technologies like Hive and Presto. Additionally, the quick cluster setup and ad-hoc query features are highlighted as valuable assets for data analysis and processing. Despite some minor concerns about speed when dealing with large datasets, overall, users find Qubole to be a reliable and effective solution for big data management.

### Data Processing and ETL
Users generally appreciate Qubole's data processing and ETL capabilities, citing benefits such as faster processing times, reduced resource wastage, and cost savings. The platform's ease of use in setting up jobs, storing internal tables for workflow failures, and sharing resources efficiently are highlighted as key advantages. While some users have encountered occasional errors during ETL processes, overall, Qubole is praised for its ability to streamline data processing tasks effectively.

### Query Execution and Optimization
Users have varied opinions on Qubole's query execution and optimization features. Some users appreciate the ease of running queries in multiple languages and the simplicity of the user interface. They find it user-friendly and efficient for querying different aspects of data. However, others express concerns about the time it takes to run queries, the lack of error prompts before execution, and the difficulty in saving work-in-process queries. Despite these mixed sentiments, Qubole's query execution and optimization capabilities seem to offer a balance of strengths and areas for improvement according to user feedback.

### Data Analytics and Insights
User sentiment regarding Qubole's data analytics and insights capabilities is generally positive, with many users praising the platform for its ease of use and effectiveness in conducting data analysis. The interactive web interface, support for various languages like SQL and PySpark, and the ability to analyze big data sets efficiently are highlighted as key strengths. Some users appreciate the real-time analytics features for enhancing client communication and product development. However, there are minor concerns raised about the platform's interface usability and occasional connectivity issues with BI tools like Tableau. Despite these drawbacks, users find Qubole to be a valuable tool for data analysis and insights generation.

### Machine Learning and AI
Users have expressed positive feedback regarding Qubole's machine learning and AI capabilities. They appreciate the ease of working with machine learning models, the ability to deploy them efficiently, and the platform's support for running ML models with auto-scaling based on variable loads. Additionally, users have highlighted the platform's role in training predictive models, executing large ETL pipelines, and facilitating complex topic modeling for machine learning tasks. The collaborative features and high computing power provided by Qubole have also been commended for enhancing machine learning training processes.

### Data Science and Analysis
Users consistently praise Qubole's data science and analysis features. They highlight the ease of working with the platform and the comprehensive understanding it provides for complex data science tasks. The support team's professionalism and quick response time are particularly lauded, making Qubole stand out in the realm of data science solutions. While some users mention that Qubole may not be as feature-rich as some competitors, the overall sentiment leans towards a positive experience with the platform, especially in terms of data extraction, data science modeling, and workflow optimization. The ability to set up ML pipelines, track job statuses, and utilize data science notebooks efficiently are key factors that contribute to users finding Qubole beneficial for their data science and analysis needs.

### Cloud Computing and Infrastructure
Users generally praise Qubole's cloud computing and infrastructure capabilities. They appreciate the ease of managing data on platforms like AWS and the efficient querying speeds provided by services like S3 and Hadoop. While some users find limitations in terms of controlling underlying infrastructure compared to other solutions like EMR, the overall sentiment leans towards Qubole being a reliable and cost-effective option for cloud-based data analytics and processing. The platform's scalability, optimized performance, and cost-efficiency are highlighted as key advantages by users, making it a popular choice for managing cloud workloads effectively.

### Cost Efficiency and Resource Management
Users generally praise Qubole for its cost-saving features and resource management capabilities. The platform's auto-scaling functionality and efficient cost optimization have been highlighted as major benefits by reviewers. While some users have expressed concerns about increasing costs with higher usage, the majority appreciate Qubole's ability to provide cost-effective solutions for their data processing needs. Additionally, the platform's proprietary autoscaling feature has been commended for significantly reducing cloud costs compared to other solutions. Users find Qubole's cost efficiency and resource management tools to be valuable assets in their data analytics and processing workflows.

### Scheduling and Automation
Users consistently highlight Qubole's scheduling and automation capabilities as key strengths of the platform. The ease of setting up jobs, quick access to internal tables for troubleshooting, and the efficiency of the scheduler are frequently commended. Additionally, the integration with tools like Airflow and the ability to run various workloads such as Hive, Spark, and Presto contribute to a positive user experience in terms of scheduling and automation. Despite some minor suggestions for improvement, users generally find Qubole's scheduling and automation functionalities to be reliable and beneficial for their data processing workflows.

### Error Handling and Debugging
User sentiment regarding Qubole's error handling and debugging features is mixed. While some users appreciate the ease of debugging and the clear error messages provided, others find the error messages to be opaque and unhelpful in resolving issues. Additionally, there are concerns about the clarity of error notifications, the speed of new feature rollouts, and the occasional errors encountered during ETL processes. Users also highlight the need for improvements in error clarity, cluster setup intuitiveness, and the handling of data visualization within the platform.

### Integration and APIs
Users consistently highlight Qubole's integration capabilities and API functionality as key strengths of the platform. The ease of integrating with various cloud service providers and the ability to effortlessly manage resources through the API are frequently commended. While some users have encountered challenges with AWS integration and SAML compatibility, the overall sentiment leans towards positive experiences with Qubole's integration and API features. The platform's support for scheduled jobs, development environments, and the execution of spark applications via the API are particularly valued by users. Additionally, the flexibility and stability of the API, coupled with features like autoscaling and cluster lifecycle management, contribute to making Qubole a preferred choice for developing big data applications.

### Development and Testing
Users generally appreciate Qubole's development and testing features. The platform offers easy setup for development code on clusters, job pipelining, testing, debugging, and resource optimization. However, some users have expressed concerns about the slow support for up-to-date versions of tools like Hive and Spark, as well as challenges in addressing data-related issues during Spark job runs. Despite these drawbacks, Qubole is praised for its interactive web interface, which simplifies data analysis and development tasks, making it a valuable tool for testing and validating hypotheses.

### User Interface and Experience
Users consistently express mixed opinions about Qubole's user interface, particularly regarding its speed and responsiveness. While some appreciate the ease of use and helpful support team, others find the UI to be slow, laggy, and in need of improvement. Issues such as sluggishness, lack of intuitiveness, and difficulties in navigation have been highlighted by reviewers. Despite some positive feedback on the platform's self-serve nature and stability, concerns about the UI's performance and functionality persist among users.

### Collaboration and Sharing
User sentiment regarding Qubole's collaboration and sharing capabilities appears to be mixed. While some users appreciate the ease of sharing queries and saved results, others find limitations in the analytical support and user interface, leading to considerations of switching to alternative platforms like Mode. The platform's history section and share-ability features are highlighted as beneficial, but challenges with versioning, code changes in shared links, and the need for additional training on new features like Analyze Beta are noted as areas for improvement. Users also express a desire for more educational resources, such as YouTube tutorials, to enhance their understanding and utilization of Qubole's collaboration tools.

Users have made the following recommendations about Qubole:

1. Take the time to learn and understand the Qubole platform. Users suggest using the trial period to become familiar with Qubole and its interface. Once users are comfortable with the platform, they find it very easy to use for data analysis and development. It is recommended to engage with Qubole's teams to understand the potential return on investment (ROI) and address any security requirements during implementation. Additionally, users advise doing thorough research to determine if a fully integrated platform is truly needed before considering Qubole.

2. Consult with Qubole support and focus on business goals. Users recommend utilizing Qubole's support team at the beginning of their journey. They suggest having a Virtual Private Cloud (VPC) setup and choosing the appropriate scale for their needs. It is also suggested to allow Qubole to manage the infrastructure while focusing on business objectives. The user highly recommends consulting with Qubole's support team and considers them helpful in navigating the platform.

3. Explore the features and consider specific use cases. Users recommend exploring all the features available in Qubole to leverage them in finding suitable solutions. They find Qubole particularly useful for handling Hive queries and suggest testing specific use cases for notebook use. Furthermore, it is advised to evaluate Qubole for real use cases and consider parallel processing and Spark workflows as prerequisites for adopting Qubole as a solution. Users also suggest utilizing the dashboards provided by Qubole for efficient data analysis.

Overall, users find value in using Qubole as a data lake platform, especially for financial analysis, and appreciate its emerging status that brings together various big data processing technologies under one roof. However, there is room for improvement in areas such as query processing speed, customer support, and documentation.

Confusing and Not User-Friendly Interface: Many users have expressed frustration with the user interface of Qubole, stating that it is confusing and not user-friendly. They have found tasks like executing queries and using the notebook feature to be difficult and time-consuming.

Cluster Management Issues and Limited ETL Tools: Some users have found cluster management in Qubole to be problematic at times. Additionally, they have mentioned that Qubole has fewer ETL tools compared to its competitors, which can be a drawback for certain data processing tasks.

Unreliable Scheduling System: Users have reported that the scheduling system in Qubole is not reliable, leading them to host their own Airflow instance. This lack of reliability can cause disruptions in their workflow and impact overall productivity.

Transparent Culture: Users have praised Qubole for its transparent culture, with several reviewers mentioning this aspect. They appreciate the open and honest communication within the company, which fosters a sense of trust and transparency between employees and management.

Great Customer Focus: Many users have highlighted Qubole's great customer focus as one of its key strengths. They feel that the platform truly values its customers and goes above and beyond to meet their needs. Reviewers mention that they receive excellent support and prompt responses from the customer service team.

Innovative Platform: The innovative platform offered by Qubole has been highly regarded by users. Multiple reviewers have mentioned how impressed they are with the features and usability of the platform. They find it easy to use, thanks to its user-friendly interface, and appreciate the ability to manage big data programmatically.

Qubole has proven to be a valuable tool for users across various industries, enabling them to query and analyze massive amounts of data in a Hadoop environment with impressive speed and efficiency. Users have leveraged Qubole to access AWS BigData Cluster, allowing for insights and analysis of big data. The software has been widely used for production ETL jobs, machine learning projects, and end-to-end ML workflows, providing ease of use and seamless integration. Moreover, Qubole has helped users save costs and resources by efficiently maintaining and cleaning big data. It has also been utilized for click stream analytics, improving product offerings for customers. The software's success extends into machine learning, streaming, and ad-hoc analytics, where it has improved success rates in ML models. With Qubole, users have been able to seamlessly work with big data from various sources, making it available across the organization. Additionally, Qubole notebooks have been used extensively for data analysis on large sensory data sets, aiding in business decision-making. Overall, the software's use cases span analytics, data quality, data processing, and much more.

Qubole is a NoSQL database offering from the California-based company of the same name.

Amazon DynamoDB is a cloud-native, NoSQL, serverless database service.

On-Demand - Write Operation

On-Demand Read Operation

On-Demand Data Stored

On-Demand Point-in-Time Recovery

On-Demand Snapshot

On-Demand Restoring a Backup

On-Demand Global Tables

On-Demand Streams

On-Demand Data Requests Outside AWS Regions

Provisioned - Write Operation

Provisioned - Read Operation

Provisioned - Data Stored

Provisioned - Point-in-Time Recovery

Provisioned - Snapshot

Provisioned - Restoring a Backup

Provisioned - Global Tables

Provisioned - Streams

Provisioned - Data Requests Outside AWS Regions

Amazon DynamoDB

Cloudant is an open source non-relational, distributed database service that requires zero-configuration. It's based on the Apache-backed CouchDB project and the creator of the open source BigCouch project.
Cloudant's service provides integrated data management, search, and analytics engine designed for web applications. Cloudant scales your database on the CouchDB framework and provides hosting, administrative tools, analytics and commercial support for CouchDB and BigCouch. 

Cloudant is often considered part of a new generation of 'NoSQL' databases that don't require fixed table schemas and is challenging the dominance of traditional relational databases (including the popular MySQL).

Lite

Standard

IBM Cloudant

Qubole

Some cloud platforms offer options for using a database as a service, without physically launching a virtual machine instance for the database. In this configuration, application owners do not have to install and maintain the database on their own. Instead, the database service provider takes responsibility for installing and maintaining the database, and application owners pay according to their usage. For example, Amazon Web Services provides three database services as part of its cloud offering, SimpleDB, a NoSQL key-value store, Amazon Relational Database Service, an SQL-based database service with a MySQL interface, and DynamoDB.

NoSQL Databases

Qubole On-Demand: Discover our Open Data Lake Platform

Qubole On-Demand - Data Engineering Demo

Qubole On-Demand - Machine Learning Demo

QUBOLE LIVE DEMO: Helping Data Engineers Operationalize Complex Streaming

Qubole On-Demand - Ad-Hoc Analytics Demo

Watch this webinar recording for a demonstration on how you can accelerate your data lake adoption, reduce time to value, and lower cloud data lake cost by 50% by implementing Qubole’s Open Data Lake platform on AWS. The benefits we explore include:
- Rapid deployment and on-demand scalability at a significantly lower cost than other solutions for implementing big-data projects in the cloud
- Automated cluster lifecycle management
- Near-zero administration
- Understand the differences between current support and Amazon’s newer offering of Graviton support.
- Fast adoption with out-of-the-box tools for data science, data engineering, and analytics

LIVE DEMO: Stop The Cloud Cost Madness! Graviton, AWS and Qubole will Reduce Your Data Lake Costs

Real-Time Qubole Data Science Demo for Retail - Data Science Festival

Likelihood to Recommend

Likelihood to Renew

Performance

Availability

Concurrency

Security

Scalability

Data model flexibility

Deployment model flexibility

Overview of Qubole's Data Platform for Self-Service AI, Machine Learning, and Analytics

What is Qubole?

The #1 Cloud-Native Data Platform for Self-Service AI, Machine Learning, and Analytics

NoSQL databases are designed to be used across large distrusted systems. They are notably much more scalable and much faster and handling very large data loads than traditional relational databases.

How fast the database performs under data load

Availability is the probability that the NoSQL database will be available to preform its function when called upon.

Concurrency is the ability for multiple processes to access or change shared data simultaneously. The greater the number of concurrent user processes that can execute without blocking each other, the greater the concurrency of the database system.

Security features include authentication against external security mechanisms liker LDAP, Windows Active Directory, and authorization or privilege management. Some NoSQL databases also support encryption.

NoSQL databases are inherently more stable than relational databases and have built-in support for replication and partitioning of data to support scalability.

NoSQL databases do not rely on rely on tables, columns, rows, or schemas to organize and retrieve data,  but use use more flexible data models to accommodate the large volume and variety of data being generated by modern applications.

Can be deployed on-premise or in the cloud.

Apache Hadoop

Presto

Apache Hive

Amazon EMR (Elastic MapReduce)

Hortonworks Data Platform

Databricks Data Intelligence Platform

Teradata Hadoop for the Enterprise

Cloudera Altus (discontinued)

Cloudera Data Science Workbench

Consultant

Qubole 2018-04-01 17:39:47

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