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V E R T I C A

The fastest & cost-effective analytical DB for SQL and ML Analytics

The Analytical DB designed for ML, speed and elasticity.

VERTICA Data Platform is a market-leading analytical database that can access any data, at any scale, anywhere. It enables organizations to modernize their Data warehouse, deploy their analytics in a hybrid cloud environment, and democratize data and analytics. Vertica is designed to deliver scalability and efficiency from the start, reducing organizations’ TCO and minimizing server requirements.

VERTICA’s optimized columnar architecture means queries run 10 to 50 times faster than on most platforms, while you can store 10 to 30 times more data per server.

VERTICA Data Platform

 VERTICA Features:

  • Standar-based Analytics. Supports SQL, R, Python, Java, C/C++ and other languages ​​so everyone in the organization can access analytics without having to write special queries or consider data structure. 
  • Data Lakehouse. Analyzes data wherever it resides (HDFS or Cloud Object Storage) and in all popular formats (ORC, Parquet, AVRO, JSON, or ROS / native Vertica) so users can gain insights faster and more efficiently.
  • Flexible Deployment. Deploy analytics anywhere, leveraging the servers and storage you already have.
  • In-Data Base Analytics. Offers 650+ built-in analytics functions including time series, event pattern matching, geospatial, and in-database ML.
  • Data security. Provides end-to-end security with support for industry-standard protocols and partner solutions such as LDAP, Kerberos, TLS, FIPS 140-2, AWS IAM, and Apache Sentry.

  • In-DB Machine Learning (ML). Develop a wide range of analytics to analyze and predict, using in-database ML to learn and decide on the largest data sets.

  • VerticaPy. It´s a Python library with scikit-like functionality used to conduct data science projects on data stored in Vertica, leveraging Vertica’s speed and built-in analytics and machine learning capabilities. VerticaPy offers robust support for the entire data science lifecycle, uses a ‘pipeline’ engine to sequence data transformation operations, and offers beautiful graphing options.

Use Cases

  IOT Analytics

Manage and analyze large volumes of IoT data for analysis, prediction, and prevention.

The “IoT” (Internet of Things) is projected to include millions, if not billions, of connected devices, generating an unfathomable amount of sensor data.

With emerging and real-world use cases – such as predictive maintenance, smart metering, telemedicine and usage-based insurance – forward-looking companies and equipment manufacturers are reducing costs, improving customer satisfaction and creating new business models based on inherent value of information.

To take advantage of this emerging megatrend, your organization must do more than simply collect large amounts of sensor data, for example in Hadoop and Data-Lakes. Your success with these new business models depends on your ability to leverage a proven, highly scalable Big Data SQL analytics platform that stores and analyzes volumes of sensor data at extreme scale.

How IOT analytics are being used:

  • Predictive Maintenance. Continuous monitoring of machine and sensor data helps equipment manufacturers and service providers predict and resolve maintenance issues before they occur.
  • Smart Buildings. Security personnel at facilities use monitors to collect data about building systems, including energy, heating, ventilation, and air conditioning (HVAC) equipment, to anticipate and troubleshoot problems before they turn into “hot” or “cold” calls.
  • Smart devices and wearables. Product developers are incorporating sensors into consumer electronics to discover user-specific insights and value-added services for a wide range of needs – from smart appliances to fitness trackers.
  • Smart Measurement. Utilities are implementing networked digital advanced metering infrastructures (AMI) to systematically monitor consumption and demand data across regions.

Customer Behavior Analytics

Customer behavior analysis enables companies across all industries to understand, engage and retain customers.

While traditional analytics technologies have helped companies understand how their customers interact with products and services in order to provide a more personalized experience and monetize more opportunities, customer expectations have increased, creating more pressure on companies to deliver better results. an even greater view of the customer in each interaction.

VERTICA Analytics Platform enables data-driven organizations across industries to manage and analyze large volumes of disparate customer data sources — from web logs and clickstreams to CRM and detailed customer call records — at exabyte scale and speed extreme view to get a detailed view and 360-degree view of customer interactions.

Armed with deeper, advanced analytical insights, your organization can ultimately improve customer web experiences, test and iterate products and services, improve marketing and campaign effectiveness, reduce acquisition costs, and reduce churn.

How Customer behavior analytics are being used:

  • Clickstream Analysis. Analyzing clickstream data provides valuable insights into which pages are effective and which pages website visitors ignore, allowing your team to make targeted changes for a better user experience. When combined with sales and conversions data, clickstream analysis can help you discover the most effective series of steps to encourage conversions, sales, and follow-up purchases.
  • Predictive Customer Churn Analysis. By combining multiple sources of customer behavior and interaction analytics data with VERTICA’s built-in machine learning algorithms, companies can better understand, identify and predict early signs of customer churn and take proactive steps to increase customer retention and lifetime value.

5G Telecom Networks Analytics

Optimize insights and AIOps with analytics and AI embedded in telecom applications and solutions

5G is transforming the telecom industry, enabling more data to flow across networks than ever before. To stay ahead and protect their reputations, telecom operators and application developers are tasked with managing and analyzing huge volumes of data more quickly and efficiently.

Embed high-performance data analytics and AI solutions into telecom applications and services to unlock better business intelligence, enable AIOps, and get products to market faster. AI in the telecom sector enables the real-time insights needed to make the most of 5G and MEC (multi-access edge computing) and provide uninterrupted services

Impact on business:

  • Data Analysis in the Telecom Industry. Telecom organizations must analyze network data to optimize services and make smarter business decisions. However, legacy analytics solutions are unable to analyze the mountains of data generated by 5G. Gain deep insights with built-in data analytics and AI.
  • Data Access. Real-time data is necessary for telecom operators to provide uninterrupted services and make informed decisions. Traditional data transfers are slow and reduce the time to gain insights. Use analytics and AI to quickly access all data from sources inside or outside the cloud.
  • Reporting and Data Visualization. Telecom operators rely on in-depth reporting and visualizations to understand structured and unstructured network data. Open source analytics solutions offer limited reporting. With VERTICA it´s possible to improve business intelligence with advanced analysis.
  • Data Management. Evolving regulations mean that data privacy is more important than ever for telecommunications companies. With more data crossing networks, it´s difficult to apply complete governance with traditional methods. Leverage AI to comply with data regulations in the 5G era.

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