84 skills found · Page 1 of 3
Teradata / CovalentCovalent - A Design System for Teradata
Teradata / KyloKylo is a data lake management software platform and framework for enabling scalable enterprise-class data lakes on big data technologies such as Teradata, Apache Spark and/or Hadoop. Kylo is licensed under Apache 2.0. Contributed by Teradata Inc.
jumpmindinc / Symmetric DsSymmetricDS is database replication and file synchronization software that is platform independent, web enabled, and database agnostic. It is designed to make bi-directional data replication fast, easy, and resilient. It scales to a large number of nodes and works in near real-time across WAN and LAN networks.
paypal / GimelBig Data Processing Framework - Unified Data API or SQL on Any Storage
Teradata / PyTdA Python Module to make it easy to script powerful interactions with Teradata Database in a DevOps friendly way.
capitalone / GiraffezUser-friendly Teradata client for Python
Teradata / Python DriverTeradata SQL Driver for Python
GoogleCloudPlatform / Datacatalog Connectors RdbmsSample code with integration between Data Catalog and RDBMS data sources.
Teradata / Sqlalchemy TeradataA SQLAlchemy dialect for Teradata
Teradata / Covalent Code Editor NightlyNightly builds of https://github.com/teradata/covalent code-editor module
sqlparser / Sql Pretty PrinterSQL beautifier for databases include but not limited to Oracle, SQL Server, DB2, Sybase, MySQL, PostgreSQL, Teradata.
paypal / PPExtensionsSet of iPython and Jupyter extensions to improve user experience
Teradata / Teradata MCP ServerThe community development of a MCP server for a Teradata database
Teradata / Docker ImagesDocker images used internally by various Teradata projects for automation, testing, etc
CatAnnaDev / Full Opcode Tera EU NANo description available
Accenture / DBTestCompareApplication to compare results of two SQL queries
Teradata / Dbt Teradatadbt adapter for Teradata
Teradata / JupyterextensionsTeradata SQL Extension for Jupyter
Aghoreshwar / Awesome Customer AnalyticsCustomer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.
xlfe / Dwhwrappercli wrapper for Teradata data warehouse utilities (BTEQ,etc..)
