The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most…… Read more...
Organizations wishing to implement a Data Strategy—a set of decisions that form a pattern, charting a high-level course of action—face significant challenges in unifying their message across the data lifecycle. Only 30% of companies have a clear organizational Data Strategy, leaving their different departments to figure out how to manage company data assets. Moreover, two out of three Data Management practices originate at a departmental level instead of from the top, leaving…… Read more...
Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, industry partnerships, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide you a one-stop source of…… Read more...
In TheSequence, we like to experiment with different formats, and today we introduce TheSequence Guest Post. Here we give space to our partners to explain in detail what machine learning (ML) challenges they help deal with. In this post, Molecula’s team talks about:Pre-aggregation strategies are workarounds to deal with data infrastructure limitations.Pre-aggregating data often creates performance problems and may mask insights that could be found with more granular data.Eliminating the…… Read more...
In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so…
Continue reading: https://insidebigdata.com/2021/10/05/insidebigdata-latest-news-10-4-2021/
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Data lakes were created in response to the need for Big Data analytics that has been largely unmet by data warehousing. The pendulum swing toward data lake technology provides some remarkable new capabilities, but can be problematic if the swing goes too far in the other direction. Far from being at the end of this evolutionary process, we are in the middle of it, said Anthony Algmin, CEO of Algmin Data Leadership, during his presentation titled Data Warehouse vs. Data Lake…… Read more...
What is Object Storage and how does it workObject storage (also known as object-based storage) is a data storage architecture that is used for storing large amounts of unstructured data.By unstructured data, we usually refer to data that cannot be arranged based on a particular data model or schema, and thus it cannot be stored on traditional relational databases. Text, image, and audio files are probably the most common types of unstructured data.Due to the increasing volume of unstructured…
Continue reading: https://pub.towardsai.net/object-storage-521d5454d2d?source=rss—-98111c9905da—4
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Click to learn more about author Gary Lyng.
In 2013, the big data headline was the incredible statistic that 90% of all data in the history of the entire human race had been created in the previous two years. The amount of structured and unstructured data we’ve created was so mind-boggling that we deemed it “big data.” Now it’s 2021 and that exponential growth has not slowed down – in fact, it has sped up. In 2020, each person generated an average of 1.7 megabytes of data per second. The sheer volume of data being created can be overwhelming to comprehend for one person – and especially for an enterprise organization. … Read more...
Data engirds the entire world. Data is evolving just like any other thing on this globe. Being a part of this tech-oriented world, today we human beings create as much information in just 2 days as we did since the beginning of time till 2003.
Amazed? Well, there’s more.
The number of data industries store and capture magnifies every 1.2 years. Nonetheless, in this modern age of technological innovations and computational advancements, we upload 200 thousand photos on Facebook,…
Continue reading: http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:1071015
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A data lake is a system or repository of data stored in its natural/raw format (Wikipedia) and it is an important trend in data world, replacing data warehouses. There are data lakes, cloud data lakes, data lakehouses, and more.
New KDnuggets Cartoon looks at a problem a data engineer may encounter when trying to measure a data lake.
How Deep is that Data Lake?
and KDnuggets posts tagged
See also other recent KDnuggets Cartoons:
Continue reading: https://www.kdnuggets.com/2021/10/cartoon-data-lake.html
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Most businesses tend to rely on relational database management systems (RDBMS) to provide business insight, including continuous intelligence. Cloud relational databases have improved computing power they bring to the table, to handle more massive amounts of data. However, relational databases, even ones in the cloud, face two issues. They have a harder time with the unstructured big data and enormous memory demands. Their fixed schema architecture makes it difficult to service a high proportion of continuous intelligence.
Gartner predicts, by 2022, more than “half or major new business systems will incorporate continuous intelligence.” Continuous intelligence requires transforming big data into real-time analytics that business operations can use to prescribe actions.… Read more...
Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this new regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace. We invite submissions with a focus on our favored technology topics areas: big data, data science, machine learning, AI and deep learning….
Continue reading: https://insidebigdata.com/2021/09/27/heard-on-the-street-9-27-2021/
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Data has outgrown the legacy technologies we’ve developed to collect, manage, and act on it to create value. Complex and time-consuming workarounds cover for these larger infrastructure problems, but they can’t meet the scale and speed required to maintain a competitive business edge. Join our webinar on Oct 6 at 2 pm EDT to learn a new way to think about your data. Hear real-life success stories of organizations that are now able to collapse data schemas on ingest, negating the need…… Read more...
Every investor wants to find the hidden unicorn in a sea of potential investments. Identifying founders, especially early in their career, with unicorn potential is extremely difficult. Though, there are many examples of attempts to predict the success of a company. I was able to find more than 20 in a quick google search. Some of these are tricks-of-the-trade from investors giving their perspective on what matters the most. Others are machine learning engineers searching for predictive insights from big data.
Unfortunately, these methods ultimately fall short. There are various reasons these methods are less than ideal. The insights investors provide are valuable perspectives into what they consider important, but they are impossible to replicate.
This year’s Big Data and AI Toronto conference and expo, held virtually Oct 13-14, will provide attendees with a 360° view of the industry through a unique 4-in-1 experience: Artificial intelligence, big data, cloud, and cybersecurity.
Since 2016, Big Data and AI Toronto has been providing a unique platform for IT decision-makers and data innovators to explore and discuss insights, showcase the latest innovative projects, and connect with other data and analytics professionals.
This year’s conference and expo, held virtually on October 13-14, 2021, will provide attendees with a 360° view of the industry through a unique 4-in-1 experience: Artificial intelligence, big data, cloud, and cybersecurity.
The benefits realized by any and every data initiative will be coupled to and limited by the maturity of an organization’s information literacy.
Ask any data leader about their data strategy; they’ll likely start with their modern data architecture, mentioning buzzwords like data lakes, event streaming, or unstructured/semi-structured data. Next, they may dive into the tech they’re using or planning to use. For example, Kafka, Fivetran, Snowflake, or Looker could all be referenced in explaining their data strategy. The data leader you’re asking might also describe how they intend to operationalize their ML-powered insights or develop complex models through their data science team.
Using data for business is no longer optional if you hope to keep up. Every day, over 2 exabytes of data are generated. There are a lot of valuable insights that can be garnered from accessing and analyzing some of that data.
Part of the reason for Big Data’s power and popularity is the speed with which it empowers your business decisions. What used to take months can now take mere seconds to pull up, analyze, and inform your decisions.
It’s difficult to overstate the power and possibilities of real-time data like what you can get from streaming data architecture.
To help give you more ideas of what you might do with stream data, we’ve put together a guide about data streaming architecture so you can learn how to use it in your organization!
To launch your data career, you’ll need both theoretical knowledge and applied skills. Bootcamp programs like Springboard’s Data Science Career Track and Data Engineering Career Track can help make you job-ready through hands-on, project-based learning and one-on-one mentorship. Wondering which data career path is right for you? Read on to find out.
Although data engineers and data scientists have overlapping skill sets, they fulfill different roles within the fields of big data and AI system development. Data scientists develop analytical models, while data engineers deploy those models in production. As such, data scientists focus primarily on analytics, and data engineers focus more heavily on programming.
You would probably agree that Java is one of the most used languages in the world. It is also one of the most successful languages in the programming world. Based on the concept of WORA (write once, run anywhere) it removes any platform dependencies during the application execution phase.
Today’s digital world is enabling companies to try out technologies that will help achieve reach new heights by uplifting excellence. And, we’re witnessing the growth of the Internet of Things (IoT) with the emerging popularity of object-oriented programming language. The emergence of the Internet of Things and the concurrent evolution of technologies, in general, has necessitated the combination of IoT with myriad other technologies such as AI, big data, etc.
eCommerce is booming, and consumer’s data has become a lifeline for online stores. A huge volume of data is generated by the eCommerce industry when it comes to customer patterns and purchasing habits.
It is projected that by 2025, the digital universe of data will reach 175 zettabytes, a 61 percent increase. It includes e-commerce – tracking shoppers’ activities, their locations, web browser histories, and abandoned shopping carts.
Modern tech such as Artificial intelligence (AI), Machine Learning, and Big Data is not just for books and sci-fi movies anymore.
This article can help companies to step into the Hadoop world, move an existing Hadoop strategy into profitability or production status.
Though they may lack functionality to which we have become accustomed, scale-out file systems that can handle modern levels of complex data are here to stay. Hadoop is the epitome of the scale-out file system. Although it has been pivoted a few times, it’s simple file system (HDFS) persists, and an extensive ecosystem has built up around it.
While there used to be little overlap between Hadoop and a relational database (RDBMS) as the choice of platform for a given workload, that has changed.
Do you use navigation software to get from one place to another? Did you buy a book on Amazon? Did you watch “Stranger Things” on Netflix? Did you look for a funny video on YouTube?
If you answered yes to any of these questions, congratulations! You are a big data producer. In fact, even if you did not answer “yes” to any of my questions, you’re probably still contributing to big data — in today’s world, where each of us has at least one smartphone, laptop, or smartwatch, smart car system, robotic vacuum cleaner, and more, we produce a lot of data in daily activities that seem trivial to us.
The finance industry generates a huge amount of data. Did you know big data in finance refers to the petabytes of structured and unstructured information that helps anticipate customer behaviors and create strategies that support banks and financial institutions? The structured information managed within an organization enables providing key decision-making insights. The unstructured information offers significant analytical opportunities across multiple sources leads that leads to increasing volumes.
The world generates a staggering 2.5 quintillion bytes of data every single day! Seeing the abundance of data we generate, most businesses are now seeking to use this data to their benefit, including the banking and finance sector.
Membrane keyboards and switches were invented several decades ago. Today membrane switches are the most effective solution for many applications. A membrane switch has become indispensable for devices used in industries ranging from medical and military to defense and manufacturing. In this article, we go over all the key information design engineers need to know about membrane switches to make the smartest choices for their applications. The first breakthrough in the history of membrane switches was the introduction of polyester as the base material. This eliminated the quality problems given that polyester can withstand 1 million operations.
Am love to share the virtual pages on which I write via my mind’s illuminating pen.
Top data science jobs are well-paying, always in-demand, and have lots of career advancements
There’s never been a better time to learn data science and enter the workforce as a data scientist. Because of the extreme inflow of big data, the data science landscape is drastically evolving and housing more professionals. Not just the tech industry, but also other sectors like healthcare, telecommunication, education, banking, manufacturing, are opening their door for data science professionals. Glassdoor has labeled data science as the second-best job in America in 2021. This marks almost a decade of data science’s undying dominance in the sphere.