Camron  Shields

Camron Shields


Share AWS Redshift data across accounts


AWS Redshift is a columnar warehouse service that is often used for massive data aggregation, correlation and can host petabyte-scale data in a clustered model. In a typical SDLC environment on the cloud, different accounts are used for different SDLC environments like Dev, Stage, Test, and Production. Like any other database system, there is a need to port data held in AWS Redshift clusters from one environment to another. As the data held in Redshift clusters can be massive in size, moving this data across multiple accounts can become challenging as well as increase cost and redundancy in other accounts.

One option for sharing data in other accounts is to extract the entire data out of redshift cluster in other services like AWS S3 and then transfer this data using online programmatic methods or offline method by transferring data on an appliance or on-premise location, and re-uploading the same data on the new account. While these methods can still achieve the purpose but are neither scalable nor cost-efficient. Also, by taking the data out of the cluster, the metadata and the model of the database objects may be lost. One of the standard methods followed to transfer data across AWS Redshift clusters within as well as across AWS accounts is by creating snapshots of the cluster and then restoring this snapshot in the cluster of choice. In this article, we will learn a mechanism to address this scenario by using AWS Redshift database snapshots, sharing it with the desired accounts and restoring the same into an Amazon Redshift cluster.

AWS Redshift Setup

In this article, we would start with a working AWS Redshift cluster and it’s assumed that you already have the required data in the cluster that is required to be shared with a different AWS account. Those who are new to AWS Redshift can refer to this article, Getting started with AWS Redshift, to create a new Redshift cluster. Once the cluster is created, it would look as shown below on the Amazon Redshift Clusters page. To simulate the scenario, it’s recommended to create some test data of a reasonable volume so that when the snapshot is created, the size of the volume is large. While it is not necessary to create sample data for this exercise, but you would be able to appreciate the value that this feature provides to sharing large sized backups across AWS accounts compared to other indirect methods of porting data across AWS accounts.

Redshift cluster

In my last article, Managing snapshots in AWS Redshift clusters, we discussed AWS Redshift manual and automated snapshots, which are used for backups and recovery. Snapshots are also a vehicle for moving data from one cluster to another as well. When one cluster restores snapshot of another cluster, data is automatically ported that is held in the backup. We need a mechanism in which snapshots of one Amazon Redshift cluster hosted in one account can be accessed by another Amazon Redshift cluster hosted in a different account. Redshift supports automated as well as manual snapshots, as we discussed in my last article, which would look as shown below in the Snapshots section of the cluster properties.


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Share AWS Redshift data across accounts
Siphiwe  Nair

Siphiwe Nair


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

Gerhard  Brink

Gerhard Brink


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.


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).

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

Database Vs Data Warehouse Vs Data Lake: A Simple Explanation

Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.

The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.

#data-warehouse #data-lake #cloud-data-warehouse #what-is-aws-data-lake #data-science #data-analytics #database #big-data #web-monetization

Cyrus  Kreiger

Cyrus Kreiger


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

Macey  Kling

Macey Kling


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