Google's Elastifile buy shows need for cloud file storage - TechTarget

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Google's Elastifile buy shows need for cloud file storage - TechTarget

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Posted: 12 Jul 2019 12:00 AM PDT

Google's acquisition of startup Elastifile underscored the increasing importance of enterprise-class file storage in the public cloud.

Major cloud providers have long offered block storage for applications that customers run on their compute services and focused on scale-out object storage for the massively growing volumes of colder unstructured data. Now they're also shoring up file storage as enterprises look to shift more workloads to the cloud.

Google disclosed its intention to purchase Elastifile for an undisclosed sum after collaborating with the startup on a fully managed file storage service that launched early in 2019 on its cloud platform. At the time, Elastifile's CEO, Erwan Menard, positioned the service as a complement to the Google Cloud Filestore, saying his company's technology would provide higher performance, scale-out capacity and enterprise-grade features than the Google option.

Integration plans

In a blog post on the acquisition, Google Cloud CEO Thomas Kurian said the teams would join together to integrate the Elastifile technology with Google Cloud Filestore. Kurian wrote that Elastifile's pioneering software-defined approach would address the challenges of file storage for enterprise-grade applications running at scale in the cloud.

"Google now has the opportunity to create hybrid cloud file services to connect the growing unstructured data at the edge or core data centers to the public cloud for processing," said Julia Palmer, a vice president at Gartner. She said Google could have needed considerably more time to develop and perfect a scale-out file system if not for the Elastifile acquisition.

Building an enterprise-level, high-performance NFS file system from scratch is "insanely difficult," said Scott Sinclair, a senior analyst at Enterprise Strategy Group. He said Google had several months to "put Elastifile through its paces," see that the technology looked good, and opt to buy rather than build the sort of file system that is "essential for the modern application environments that Google wants to sell into."

Target workloads

Kurian cited examples of companies running SAP and developers building stateful container-based applications that require natively compatible file storage. He noted customers such as Appsbroker, eSilicon and Forbes that use the Elastifile Cloud File Service on Google Cloud Platform (GCP). In the case of eSilicon, the company bursts semiconductor design workflows to Google Cloud when it needs extra compute and storage capacity during peak times, Elastifile has said.

"The combination of Elastifile and Google Cloud will support bringing traditional workloads into GCP faster and simplify the management and scaling of data and compute intensive workloads," Kurian wrote. "Furthermore, we believe this combination will empower businesses to build industry-specific, high performance applications that need petabyte-scale file storage more quickly and easily."

Elastifile's Israel-based engineering team spent four years developing the distributed Elastifile Cloud File System (ECFS). They designed ECFS for hybrid and public cloud use and banked on high-speed flash hardware to prevent metadata server bottlenecks and facilitate consistent performance.

Elastifile emerged from stealth in April 2017, claiming 25 customers, including 16 service providers. Target use cases it cited for ECFS included high-performance NAS, workload consolidation in virtualized environments, big data analytics, relational and NoSQL databases, high-performance computing, and the lift and shift of data and applications to the cloud. Elastifile raised $74 million over four funding rounds, including strategic investments from Dell Technologies, Cisco and Western Digital.

One open question is the degree to which Google will support Elastifile's existing customers, especially those with hybrid cloud deployments that did not run on GCP. Both Google and Elastifile declined to respond.

Cloud NAS competition

The competitive landscape for the Elastifile Cloud File Service on GCP has included Amazon's Elastic File System (EFS), Dell EMC's Isilon on GCP, Microsoft's Azure NetApp Files, and NetApp on GCP.

"Cloud NAS and cloud file systems are the last mile for cloud storage. Everybody does block. Everybody does object. NAS and file services were kind of an afterthought," said Henry Baltazar, research director of storage at 451 Research.

But Baltazar said as more companies are thinking about moving their NFS-based legacy applications to the cloud, they don't want to go through the pain and the cost of rewriting them for object storage or building a virtual file service. He sees Google's acquisition of Elastifile as "a good sign for customers that more of these services will be available" for cloud NAS.

"Google doesn't really make infrastructure acquisitions, so it says something that Google would make a deal like this," Baltazar said. "It just shows that there's a need."

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Posted: 21 Apr 2020 12:00 AM PDT

Most organizations today understand the importance of a data-driven business procedure and are needing skilled candidates to give knowledge into the steady stream of structured data. Research shows that almost 70 percent of U.S. experts say they will lean towards candidates with data skills by 2021, and the interest for analysts will just develop as we keep on digitizing our physical world.

In case you're searching for ways to build your effect in the workforce and become a progressively attractive competitor in your industry, adjusting your data analysis skills could change you into the pined for the candidate organizations are looking for. Before finding out about some of the in-demand skills you could add to your resume, it's a smart thought to investigate what's in store for data analysts. Filtering work postings for data-driven positions is an extraordinary beginning stage, yet numerous analyst jobs are exceptionally nuanced, making it hard to recognize which skills are the most important to put time and money into. First and foremost, you should choose between Data Science, Big Data, and Data Analysis. If you are more inclined towards data analytics then continue reading,

Data Analyst Role

Data analysts transform data into information. They assume an essential role by transforming data into insights.
Data analysts frequently use data given by data scientist, examine it, make models, and suggest solutions. They make models to show their discoveries in dashboards and spreadsheets.
In contrast to data scientists, data analysts don't make prescient models dependent on AI algorithms.

In-Demand Data Analyst Skills

Now, let's jump into it:

Academic Background

A solid foundation in Computer science is a pre-requisite for data scientists. Having a professional academic degree in Social science, physical science, statistics and mathematics can also prove to be a kick starter. A higher degree can take you to the next level as well as a data analysis certification, it's up to you to decide which one's the best and feasible option for you based on your current job. The skills which are gained from these certifications hugely support the jobs in data analysis.

2- Microsoft Excel

When you think of Excel, the primary thing that strikes a chord is likely a spreadsheet, however there's much more analysis power in the engine of this device. While a programming language like R or Python is more qualified to deal with a huge data set, advanced Excel methods like composing Macros and utilizing VBA lookups are still broadly utilized for smaller data sets and quick analysis. If you are working at a lean organization or startup, the main rendition of your database may even be in Excel. Throughout the years, the device has stayed a pillar for businesses in each industry, so learning it is an absolute necessity. Fortunately, there is a bounty of incredible free resources online to assist you with the beginning.

3- Data Visualizations

Data visualizations make patterns and examples in data more obvious. People are visual animals, and a great many people won't be ready to get important understanding by taking a gander at a goliath spreadsheet of numbers. As a data analyst, you should have the option to make plots and outlines to help convey your data and discoveries visually.

This implies making clean, visually convincing outlines that will help other people comprehend the data. It likewise implies keeping away from things that are either hard to decipher (like pie outlines) or can be misdirecting (like controlling pivot values). Visualizations can likewise be a significant piece of data analysis. It's extremely uncommon to discover data job that doesn't require data visualization, making it a key data analyst aptitude.

4. Machine learning

Machine learning skills are an obvious prerequisite. With advancement in AI, an ever-increasing number of analytic tasks will be appointed to smart frameworks that can identify designs in data, yet also, learn with understanding and improve their performance. You ought to be aware of its technique if you're working at an organization that deals with data-driven products (for instance Google Maps, Uber, Netflix, Amazon, Southwest carriers). Machine learning methods are usually implemented in R and Python and data scientists can use the pre-structured libraries.

5- Quantitative Skills

Quantitative skills, which fuses the crucial comprehension of math and statistics, contributes a lot to a huge activity in data science. In the space of data science, we direct certified issues and evaluate them into data from which noteworthy bits of data can be expelled. An authentic analysis of data is conceivable when you have an overwhelming perception of genuine analysis procedures.

6- Professional Certifications

Certifications are a surprising development in your portfolio. Different legitimate organizations lean toward masters with data science certifications. Their game become possibly the most noteworthy factor when higher degrees stop, which deduces you can never again acquire a higher type of degree any longer.

7- Problem Solving Skills

As a data analyst, you're going to clash with issues, bugs, and obstacles each day. Having the option to take a solution out of them is a key aptitude. You may need to play smart rather than hard. A smart approach will solve problems for you. Your organization may have resource limitations that compel you to be creative by the way you approach an issue. The data you're dealing with may be inadequate. Or then again you may need to play out some "adequate" analysis to comply with an approaching time constraint.

8- Strong Communication Skills

You must be a sharp communicator to be a compelling data analyst. You should ask what, when and why and the reaction to this is extraordinarily clear. Data analysts don't just interface with computers, in any case, they likewise collaborate with associates and supervisors. Exceptional listening skills are equally respected while working.

These skills play a key role in the data analysis profession and you won't realize it until you step into this profession practically.

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