In our last blog topic on data lineage "Top 6 Open Source Data Lineage Tools", we discussed on what is data lineage and importance of data lineage along with top open-source & paid data lineage tools. In this blog, we will cover the top 10 real-life data lineage examples. This blog will focus on the significance and benefits of data lineage for below mentioned companies. Standard Chartered, a British multinational bank, needs no formal introduction. The bank is one of the global leaders not only in terms of the users but also in terms of its data analytics sophistication.

Today's announcement was made from the EDUCAUSE Annual Conference taking place this week in Chicago, IL. To learn more about SnapLogic for higher education, stop by SnapLogic Booth #1114 on the conference showfloor. Today's progressive universities and colleges are embracing the cloud, unifying their applications and systems, and putting data at the center of their strategies to enrich the experience of their diverse constituents: Student Engagement: The majority of incoming students are digital natives who expect consistent, real-time access to information on housing, parking, class schedule, grades, financial aid, and more, ideally delivered via a one-stop-shop online portal. Data-driven Faculty: Faculty are leveraging digital tools to tailor, personalize, and optimize learning for students, both in the classroom and via online courses. At the individual student level, many professors are leveraging data to identify students who may be struggling and require additional attention.

Manually developing and testing code on Spark is complicated and time-consuming, and can significantly delay time to market. A visual low-code solution, on the other hand, can simplify and accelerate Spark development. StreamAnalytix Lite, a light-weight, self-service data flow, and analytics platform, has transformed the experience of developing and running Spark applications, making it visual, faster and easier – right on your desktop, at no cost. StreamAnalytix Lite, a developer edition of the StreamAnalytix Enterprise Edition, retains all its capabilities and features to build, test and run enterprise-grade Spark applications 10x faster vs. hand coding. It offers an intuitive drag-and-drop visual interface to instantly transform your journey with Spark on a desktop or a single node. With its new release, StreamAnalytix Lite has further enhanced Spark development with a richer set of connectors, 150 built-in Spark operators, interactive development, enhanced self-service features, and higher collaboration for multiple users.

The service being provided by MemSQL targets the massive wave of applications that are now being deployed in the cloud. MemSQL today unfurled a preview of a cloud service based on a database optimized for translytical applications. The MemSQL Helios service is based on the beta edition of a forthcoming 7.0 release of the MemSQL database, a distributed relational database optimized to run operational analytics applications in memory. The latest version of MemSQL also provides for the first time a "SingleStore" capability that eliminates the need to choose between a row store or a column store for different classes of workloads. That capability should reduce a lot of the performance tradeoffs organizations now make when building and deploying translytical applications, says Peter Guagenti, chief marketing officer for MemSQL.

Paxata, the pioneer in self-service data preparation, today announced that it was named an innovator in Enterprise Management Associates (EMA) "Innovation in the Use of Artificial Intelligence (AI) and Machine Learning (ML) for Data Integration and Preparation" Top 3 report. According to the findings, more than half of all participants (52 percent) said that the use of AI or ML to automate the data preparation or integration process is important to their organization. Because of the prominent role of data integration and preparation in any analytics project, the report stated that AI-enablement should be a priority for analytics leaders at all levels as it provides organizations with the ability to overcome the constraints of legacy or less-automated data processing. The complimentary report can be downloaded here. "The next major shift in the analytics, business intelligence, and data management markets is coming from the use of AI and ML across the entire information supply chain. Along with using machine learning to find the next-best offer, companies can now point algorithms at modern data platforms to find links between data sets, automate data preparation, or breaches in data governance," said John Santaferraro, Research Director at EMA and lead author of the report.

Work with business users and data analysts to design and implement data integration flows into the enterprise warehouse. Use parallel processes and architectures in translating business requirements into data models. Work experience must have included: Computer Information Systems or related field and 3 years of experience in data warehousing and/or business intelligence systems. Other good to have familiarity or experience with Spark SQL/streaming, Java, Scala.

Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data Boyi Liu 1,4, Lujia Wang 1, Ming Liu 2 and Cheng-Zhong Xu 3 Abstract -- Humans are capable of learning a new behavior by observing others perform the skill. Similarly, robots can also implement this by imitation learning. Furthermore, if with external guidance, humans can master the new behavior more efficiently. So how can robots achieve this? T o address the issue, we present Federated Imitation Learning (FIL) in the paper . Firstly, a knowledge fusion algorithm is proposed for the cloud fusing knowledge from local robots. Then, a knowledge transfer scheme is presented to facilitate local robots acquiring knowledge from the cloud. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning in accuracy and training efficiency. FIL considers information privacy and data heterogeneity when robots share knowledge. It is suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a simplified self-driving task for robots (cars). The experimental results demonstrate that FIL increases imitation learning efficiency and accuracy of local robots in cloud robotic systems. I. INTRODUCTION In tradition imitation learning scenarios, demonstrations provide a descriptive medium for specifying robotic tasks. Prior work has shown that robots can acquire a range of complex skills through demonstration, such as table tennis [1], drawer opening [2], and multistage manipulation tasks [3]. Nevertheless, there exists a number of problems in the application of imitation learning.

Apache Kafka is a scalable and fault tolerant messaging system common in publish and subscribe (pub/sub) architectures. Apache Kafka is used for a range of use cases including message bus modernization, microservices architectures and ETL over streaming data. High throughput -- Each server is capable of handling 100s MB/sec of data. High availability -- Data can be stored redundantly in multiple servers and can survive individual server failure. High scalability -- New servers can be added over time to scale out the system.

As Chief Data Officer for the OSTHUS Group, Eric Little co-founded LeapAnalysis, a new approach to AI, data integration and analytics. LeapAnalysis is the first fully federated and virtualised search and analytics engine that runs on semantic metadata. It allows users to combine semantic models (ontologies) with machine learning algorithms to provide customers with unparalleled flexibility in utilizing their data. Nearly all technologies surrounding AI and analytics are purely statistical in nature, using algorithmic approaches that are not incredibly novel, such as decision trees, neural networks, etc. The logical framework that contextualises these things is often missing.

To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.