Dependencies in Azure Data Factory

In the previous articles of this series, we showed how to create Azure Data Factory pipelines that consist of multiple activities to perform different actions, where the activities will be executed sequentially. This means that the next activity will not be executed until the previous activity is executed successfully without any issue.

In this article, we will show how to control the dependencies between the activities and the pipeline runs in the Azure Data Factory.

Dependency between Activities

As mentioned previously, the default behavior of the pipeline activities is that it will not be executed unless the previous activity is executed successfully. This default dependency type in the Azure Data Factory activities is the Success type, with the green box icon and green arrow, as shown below:

If you click on any activity, you will see a number of icons that can be used to drop the activity, view the source code, and clone that activity. At the most right of these icons you will the  alone icon, which is used to control the dependency between the current activity and the next activities, where you can add and configure it to run the next activity if the current activity succeeded, failed, completed regardless of the result and finally skipped and not executed, as shown below:

#azure #sql azure #azure data factory

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Dependencies in Azure Data Factory

In the previous articles of this series, we showed how to create Azure Data Factory pipelines that consist of multiple activities to perform different actions, where the activities will be executed sequentially. This means that the next activity will not be executed until the previous activity is executed successfully without any issue.

In this article, we will show how to control the dependencies between the activities and the pipeline runs in the Azure Data Factory.

Dependency between Activities

As mentioned previously, the default behavior of the pipeline activities is that it will not be executed unless the previous activity is executed successfully. This default dependency type in the Azure Data Factory activities is the Success type, with the green box icon and green arrow, as shown below:

If you click on any activity, you will see a number of icons that can be used to drop the activity, view the source code, and clone that activity. At the most right of these icons you will the  alone icon, which is used to control the dependency between the current activity and the next activities, where you can add and configure it to run the next activity if the current activity succeeded, failed, completed regardless of the result and finally skipped and not executed, as shown below:

#azure #sql azure #azure data factory

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

• Industrial metrology for quality assurance.
• 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data