Improve your skills - "Apache NiFi Complete Master Course - HDP - Automation ETL" - Check out this online course - Apache Nifi Apache Nifi is next generation framework to create data pipeline and integrate with almost all popular systems in the enterprise. It has more than 250 processors and more than 70 controllers. As part of production maintenance, user may have to take cautious decision to improve the performance and handle the errors efficiently. To have seamless experience with data, handling of data latency and throughput and prioritizing the data is important. Its controlled with relationship, yield and back pressure. Various processors and controllers to process various type of data is demonstrated.

With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community. Yet collaborating SLAM over multiple robots still remains challenging due to performance contradiction between the intensive graphics computation of SLAM and the limited computing capability of robots. While traditional solutions resort to the powerful cloud servers acting as an external computation provider, we show by real-world measurements that the significant communication overhead in data offloading prevents its practicability to real deployment. To tackle these challenges, this paper promotes the emerging edge computing paradigm into multi-robot SLAM and proposes RecSLAM, a multi-robot laser SLAM system that focuses on accelerating map construction process under the robot-edge-cloud architecture. In contrast to conventional multi-robot SLAM that generates graphic maps on robots and completely merges them on the cloud, RecSLAM develops a hierarchical map fusion technique that directs robots' raw data to edge servers for real-time fusion and then sends to the cloud for global merging. To optimize the overall pipeline, an efficient multi-robot SLAM collaborative processing framework is introduced to adaptively optimize robot-to-edge offloading tailored to heterogeneous edge resource conditions, meanwhile ensuring the workload balancing among the edge servers. Extensive evaluations show RecSLAM can achieve up to 39% processing latency reduction over the state-of-the-art. Besides, a proof-of-concept prototype is developed and deployed in real scenes to demonstrate its effectiveness.

In this article, I will talk about my experience on scheduling data science project's notebooks on AWS Sagemaker instances using Airflow. We have been using Netflix's papermill library to run Jupyter notebooks more than 2 years now in production and everyday 10s of Sagemaker Notebook instances are orchestrated by Airflow working like a charm. You will read about the general architectural design of this system, what is the way of working, what are the roles and responsibilities between teams and how you can implement it yourself. It all started with me reading this article on Netflix blog about running jupyter notebook files with external parameters for productionizing data science workloads. This could be the solution to a common problem which I faced in my previous company, we were running Apache Spark applications using pyspark and other python code for data science and reporting projects on AWS EMR.

Leading analysts and organisations have begun recognising data warehouse automation as being key to running a truly data-driven business. AI News caught up with Rob Mellor, GM & VP, EMEA at WhereScape, to discuss this industry shift. AI News: Only earlier this year did Gartner really begin recognising data warehouse automation after publishing a paper on the subject. Is this indicative of a shift in how companies view automation? Rob Mellor: At WhereScape, we feel the increased recent activity from Gartner around data warehouse automation is reflective of an industry shift.

Extract, transform, and load (ETL) is the process by which data is acquired from various sources. The data is collected in a standard location, cleaned, and processed. Ultimately, the data is loaded into a datastore from which it can be queried. Legacy ETL processes import data, clean it in place, and then store it in a relational data engine. "SQL Server Integration Services is a platform for building enterprise-level data integration and data transformations solutions. Use Integration Services to solve complex business problems by copying or downloading files, loading data warehouses, cleansing and mining data, and managing SQL Server objects and data."

Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.

Fusion technique is a key research topic in multimodal sentiment analysis. The recent attention-based fusion demonstrates advances over simple operation-based fusion. However, these fusion works adopt single-scale, i.e., token-level or utterance-level, unimodal representation. Such single-scale fusion is suboptimal because that different modality should be aligned with different granularities. This paper proposes a fusion model named ScaleVLAD to gather multi-Scale representation from text, video, and audio with shared Vectors of Locally Aggregated Descriptors to improve unaligned multimodal sentiment analysis. These shared vectors can be regarded as shared topics to align different modalities. In addition, we propose a self-supervised shifted clustering loss to keep the fused feature differentiation among samples. The backbones are three Transformer encoders corresponding to three modalities, and the aggregated features generated from the fusion module are feed to a Transformer plus a full connection to finish task predictions. Experiments on three popular sentiment analysis benchmarks, IEMOCAP, MOSI, and MOSEI, demonstrate significant gains over baselines.

Flexiv, the world leader in general-purpose robotics, has received CE and ETL approval for their Rizon 4 robot, making it the first-ever seven-axis force-controlled adaptive robot to achieve both certifications at the same time. "The CE and ETL certification is an essential regulatory milestone on Flexiv's road to commercialization" Demonstrating the intrinsic safety of the Rizon 4 robot, the CE and ETL approval was awarded by the world's foremost testing, inspection, certification and assurance provider, Intertek. Accepted in the EU and Northern America, the approval enables Flexiv to distribute the Rizon 4 in the European Union, Canada, and the USA. Meeting or exceeding the strict CE and ETL requirements, the Rizon 4 was subjected to hundreds of individual testing, evaluations, and assessments focused on machinery safety, electrical safety, functional safety, environmental reliability, electromagnetic compatibility, and collision detection. During the testing the Rizon 4 was also exposed to temperatures ranging from 0-45 C, dust particulates, and water jets from any direction.

Everyone: What do Data Engineers do? Everyone: You mean like a plumber? Data Scientists build models and Data Analysts communicate data to stakeholders. So, what do we need Data Engineers for? Little do they know, without Data Engineers, models won't even exist.

Land surface temperature (LST) is a key parameter when monitoring land surface processes. However, cloud contamination and the tradeoff between the spatial and temporal resolutions greatly impede the access to high-quality thermal infrared (TIR) remote sensing data. Despite the massive efforts made to solve these dilemmas, it is still difficult to generate LST estimates with concurrent spatial completeness and a high spatio-temporal resolution. Land surface models (LSMs) can be used to simulate gapless LST with a high temporal resolution, but this usually comes with a low spatial resolution. In this paper, we present an integrated temperature fusion framework for satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m spatial resolution and half-hourly temporal resolution. The global linear model (GloLM) model and the diurnal land surface temperature cycle (DTC) model are respectively performed as preprocessing steps for sensor and temporal normalization between the different LST data. The Landsat LST, Moderate Resolution Imaging Spectroradiometer (MODIS) LST, and Community Land Model Version 5.0 (CLM 5.0)-simulated LST are then fused using a filter-based spatio-temporal integrated fusion model. Evaluations were implemented in an urban-dominated region (the city of Wuhan in China) and a natural-dominated region (the Heihe River Basin in China), in terms of accuracy, spatial variability, and diurnal temporal dynamics. Results indicate that the fused LST is highly consistent with actual Landsat LST data (in situ LST measurements), in terms of a Pearson correlation coefficient of 0.94 (0.97-0.99), a mean absolute error of 0.71-0.98 K (0.82-3.17 K), and a root-mean-square error of 0.97-1.26 K (1.09-3.97 K).