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The Distributed Cloud and Data Governance

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The business of Data Management embraced new complexities when diverse types of data started flowing in—in huge volumes through multiple data channels and in real time. Analysis of very high-speed, high volume, multi-type business data necessitated the growth and development of advanced Data Management technologies and tools, and cloud computing technologies were born out of that necessity. Then came the era of multi-cloud and hybrid cloud environments, after a single, public or private cloud network failed to deliver the desired business outcomes.

Data Management typically comprises highly complex tasks such as data storage, data integration, data quality management, data security, and database management. With the rising speed and volume of data, diversity of data types, and unlimited data channels (sensor data), Data Management soon turned into a nightmare begging for effective technology solutions. To add fuel to the fire, the problems of transferring data from data stores to remote servers became an unmanageable headache for most businesses.

It was forecasted that the cloud-based services market will cater to “90% of organizations by 2022.” In spite of the tremendous potentials of the cloud platforms, the cloud service providers also face quite a few challenges. In the current data-first and AI-first era, where real-time data analytics rule the business landscape, the complex challenges of business analytics are:

  • Siloed data repositories preventing seamless data integration
  • Poor quality data resulting from the rise of data sources, data types, and data volumes
  • Absence of qualified Data Science personnel
  • Absence of clearly defined data governance (DG) policies

Thus, businesses have started looking for technology solutions in terms of Data Management platforms and tools that can address all the above challenges. This also translates to a comprehensive Data Management strategy that takes into account Data Quality, Data Governance, and complex cloud infrastructures for the future.

Data Governance Challenges of Multi-Cloud

Think of a business scenario, where the customer has to manage multiple business units—each equipped with its own edge computing environment hosted and managed by a unique cloud service provider. Managing such geographically and operationally dispersed data can create a huge Data Management malfunction. The biggest benefit of multi-cloud is agility—the ability to deliver solutions when and where businesses need them.

In a hybrid cloud infrastructure, resources are shared over on-premise, private cloud, and public cloud environments. The biggest obstacle to smooth running of operations in Hybrid Cloud is lack of governance and regulatory compliance.

Mitigating the Challenges of Data Management in Hybrid Cloud states that though hybrid clouds offer future solutions, the issues of data security and compliance on hybrid networks are trouble spots that enterprises must be prepared to handle.

And yet in a multi-cloud environment or in a hybrid cloud, although all computing resources are spread over wide-area networks; the resource management (servers) is somewhat fragmented, adversely affecting the flow of computing services. Consequently, these types of cloud computing setups lead to resource-management inconsistencies and errors that impact the overall network-performance quality. Moreover, these cloud networks now face serious compliance and governance issues in an increasingly regulated IT world. Challenges of Data Governance in a Multi-Cloud World explains some of the DG best practices that businesses can adopt in a multi-cloud environment.

Data Governance Challenges of the Cloud Warehouse

The growing importance of the data warehouse is echoed in a Mordor Intelligence Report, which indicates that the data warehouse market is growing at a “CAGR of 11.17% from USD 6.3 billion in 2019 to USD 11.95 billion by 2025.” On the cloud, the data warehouse development is further simplified and accelerated. However, data governance and security continue to remain two critical aspects that need attention.

According to Balaji Ganesan, CEO of Privacera:

“To become as decentralized and heterogeneous as the data landscape is today, data governance requires central administration, but local enforcement. This really means that the actual enforcement is done by databases and applications as close to the data as possible, not putting in another layer which becomes a single point of failure.

The Distributed Cloud Emerges as the Final Winner

To clarify the major difference between hybrid cloud and distributed cloud, a Gartner Analyst commented:

“One part of a hybrid cloud is architected, owned, controlled and operated by the customer and the other by the public cloud provider. The customer retains responsibility for their part of the operation but cannot leverage the capabilities (such as the skills, innovation pace, investments and techniques) of the public cloud provider. Distributed cloud, the next generation of cloud computing, retains the advantages of cloud computing while extending the range and use cases for cloud. CIOs can use distributed cloud models to target location-dependent cloud use cases that will be required in the future.”

So, what is the distributed cloud environment? The distributed cloud facilitates the coexistence of multiple public cloud networks across geographies, combining on-premise data centers, remote cloud provider’s networks, and other third-party locations. However, the management is centrally controlled from a single point.

In a distributed cloud network, this problem has been tackled well by provisioning edge computing—allowing both the servers and applications to execute very close to the location of data, thus improving speed, quality, and performance of business analytics by several notches. Edge computing, more significantly, received a business push with the emergence of advanced data technologies like the big data, the internet of things (IoT), and artificial intelligence (AI).

Edge helps to combat the compliance issues previously overlooked in multi-cloud or hybrid cloud. Distributed cloud and edge computing jointly enable consistent and complaint application flows across all systems in a complex multi-cloud environment.

The biggest benefit of the distributed cloud operation is that it extends the capabilities and services of the central cloud to remote, satellite networks. Who is the biggest beneficiary? The customer of course, who now manages all the business computing needs across multiple, geographically dispersed business units from his location on a “single control plane.”

Empowering the Data Consumer  

In the self-service data analytics age, “empowering the ordinary business consumer” is the central business focus. While data regulations like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) have put tremendous pressure on global businesses to comply or else, some newer challenges like enterprise data literacy have pushed organizations to break down data silos and move toward technologically enabled data-sharing capabilities.

Going Forward: Distributed Data Stewardship Is the Answer

Roadmap to Distributed Data Stewardship indicates that given the exponential rise of data in organizations, it is just not possible for centralized IT teams to manage and govern the enterprise-wide data. To ensure well-governed and well-regulated data access for all its users, enterprises “must shift toward a distributed data stewardship model.”

In the distributed data stewardship model, Data Management roles and responsibilities are shared across the enterprise. In this new scenario, decentralized teams of experts will “manage data access and permissions while eliminating the bottleneck that currently exists with centralized IT.”

This sounds easier than done, and will probably take some years to evolve into a mature working solution. The Data Governance platforms of the future will need comprehensive rules for data handling, which encompasses the core issues of security, compliance, among other Data Management tasks.

Image used under license from Shutterstock.com

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Source: https://www.dataversity.net/the-distributed-cloud-and-data-governance/

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Proximity labeling: an enzymatic tool for spatial biology

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In this Forum, we highlight how cutting-edge, proximity-dependent, enzymatic labeling tools, aided by sequencing technology developments, have enabled the extraction of spatial information of proteomes, transcriptomes, genome organization, and cellular networks. We also discuss the potential applications of proximity labeling in the unexplored field of spatial biology in live systems.

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Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00211-0?rss=yes

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Synthetic biology applications of the yeast mating signal pathway

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Glossary

Central carbon metabolism (CCM)

as the main source of energy, CCM oxidizes carbon through glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle.

Chassis

a cell host or an organism for the production of biochemicals such as enzymes by introducing synthetic modules or devices into the cell.

Circuit

an assembly of biological parts that enables cells to perform logical functions, such as genetic switches, oscillators, and logic gates.

Convolutional neural network

a class of artificial neural networks with multiple building blocks that automatically and adaptively learn spatial hierarchies of features through back-propagation.

Clustered regularly interspaced short palindromic repeats (CRISPR)

a genome-editing tool in which CRISPR-associated nuclease 9 (Cas9)–guide RNA (gRNA) complexes recognize a protospacer adjacent motif through base-pairing and then cleave the target DNA,

CRISPR activation or interference (CRISPRa/i)

a tool that uses dead Cas protein and gRNA to activate or repress genes, resulting in gene upregulation or downregulation, respectively.

Cubic ternary complex model

an equilibrium model that describes the interactions between receptor and ligand. This model simulates the interactions of G proteins and receptors in both their active and inactive conformations.

G proteins

heterotrimeric G protein complexes are composed of α, β and γ subunits. Replacement of GDP by GTP in Gα causes a conformational change that dissociates the Gβγ subunits, leading to the activation of downstream signaling.

G protein-coupled receptor (GPCR)

a generic class of versatile, seven transmembrane-domain proteins that regulate a diverse array of intracellular signaling cascades in response to hormones, neurotransmitters, and other stimuli.

Karyogamy

a cascade of molecular events that finally lead to fusion of the nuclei and the formation of diploid cells.

Metabolic engineering

a new scientific field that combines multi-gene recombination technology with metabolic regulation and biochemical engineering to overproduce desired products.

Mitogen-activated protein kinases (MAPKs)

a family of serine/threonine kinases that convert extracellular signals into a diverse range of cellular responses.

Omics

studies include genomics, transcriptomics, proteomics, and metabolomics that characterize and quantify pools of biological molecules, and together give rise to the field of integrative genetics.

Oscillator

a genetic circuit where oscillation is generated by the inhibition and activation of transcriptional/translational feedback loops.

Pheromone-response element (PRE)

a cis element that is present in multiple copies in the promoters of a variety of pheromone-responsive genes; PREs interact with Ste12 to initiate the transcription of pheromone-induced genes.

Quorum sensing

a cell density-dependent phenomenon in which cells adapt their behavior by synthesizing, secreting, perceiving, and reacting to small diffusible signaling molecules termed autoinducers.

Scaffold protein

proteins that recruit other proteins to form a functional unit, thus enhancing signaling efficiency and fidelity.

Ste5ΔN-CTM

a Ste5 mutant that lacks the Gβγ-binding site because its N-terminus has been truncated; Ste5ΔN-CTM is no longer recruited to the plasma membrane following pheromone treatment.

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Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00210-9?rss=yes

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Biotechnology of functional proteins and peptides for hair cosmetic formulations

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    Source: https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(21)00213-4?rss=yes

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    VW’s 9-month electric vehicle deliveries to China more than triple

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    FRANKFURT (Reuters) – Volkswagen’s deliveries of battery-powered electric vehicles to China more than tripled in the first nine months of the year, the carmaker said on Friday, less than two months after it flagged the need to change its e-car strategy there.

    Deliveries of battery electric vehicles (BEV) to the world’s largest car market stood at 47,200 in the January-September period, up from 15,700 in the same period last year.

    “As planned, we significantly accelerated the BEV market ramp-up in China in the third quarter, and we are on track to meet our target for the year of delivering 80,000 to 100,000 vehicles of the ID. model family,” Christian Dahlheim, head of group sales, said.

    Volkswagen Chief Executive Herbert Diess in July said the carmaker had to change its approach to how it markets its BEVs in China after first-half deliveries stood at just 18,285.

    (Reporting by Christoph Steitz; Editing by Maria Sheahan)

    Image Credit: Reuters

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    Source: https://datafloq.com/read/vws-9-month-electric-vehicle-deliveries-china-triple/18644

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