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Apache Spark has become the most commonly used tool in the Big Data universe today. It has the capability of running solo code, extending APIs to Python, Scala, Java, and many more tools. It can be used to query datasets, and the most inspiring part of its architecture is the capability of running analysis on Real-Time Streaming data without explicitly storing it anywhere. Spark originated from Scala and was designed as a distributed cluster-computing software framework. From resource management, multithreading, task distribution to actually running the logic, Spark does everything under the hood. From an end-user perspective, it is an analysis tool where huge amounts of data can be fed and required analyses can be drawn within minutes. But, how does Spark achieve it? What are some core principles of using Spark to work with large datasets?
To ramp up on the basics of Spark, its architecture, and implementation in the Big Data and Cloud world, refer to the story linked below.
#spark #data-science #data #big-data #functional-programming
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Big data is less about size, and more about how we understand data. In order to better understand data, first we need to understand the problem.
#big-data #data-engineering #data-science
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MongoDB is a modern general-purpose database platform founded in 2007 by Dwight Merriman, Eliot Horowitz and Kevin Ryan. Earlier, Google had acquired the trio’s first venture, DoubleClick–an internet advertising company. The company used to serve 400,000 ads per second, a nightmare in terms of scale and agility. This struggle proved to be the inspiration behind setting up MongoDB.
https://analyticsindiamag.com/how-mongodb-emerged-as-a-leading-general-purpose-data-platform-in-12-years/
#big-data #mongodb
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The business impact companies are making with big data analytics is driving investment in digital transformation across the board.
Faced with multiple waves of disruption in a COVID-19 world, almost 92% of companies are reporting plans to spend the same or more on data/AI projects, according to a recent survey from NewVantage Partners.
Small wonder.
Data mature companies are citing business-critical benefits from using big data, including:
Offensive drivers such as new competitive advantages, innovation, and transformation override defensive ones as change is becoming the only market constant nowadays.
Let’s explore what business benefits exactly companies are achieving with big data to edge out the competition.
#big-data #big-data-analytics #big-data-processing #big-data-trends #technology #artificial-intelligence #big-data-industry #good-company
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The massive influx of data and the growing need for modern analytics call for a modern cloud data warehouse. To thrive in today’s data-driven economy, organisations need a cost-effective cloud data warehouse that is easy to deploy, handles all types of data latencies, and supports thousands of concurrent users and queries per second.
#data #big-data #cloudcomputing
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Coursera Machine Learning Certificate Roadmap ✔️.
Step 1: Begin Your Data Science Journey
IBM Data Science Professional Certificate: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Fibm-data-science
Step 2: Machine Learning Fundamentals
Andrew Ng / Stanford University Course: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning
OR
University Of Washington ML Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine-learning
(Optional) Step 2a: If You Want To Learn ML Math
Imperial College London Math For Machine Learning: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmathematics-machine-learning
(Optional) Step 2b: If You Want To Master Programming
Stanford University Algorithms Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Falgorithms
Step 3: Deep Learning Fundamentals
Andrew Ng Deep Learning Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdeep-learning
Step 4: Apply Your ML Skills
TensorFlow Deep Learning Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Ftensorflow-in-practice
Step 5: Move To The Cloud
Amazon Web Services Data Science Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fpractical-data-science
OR
Google Cloud Big Data & ML On GCP: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fgcp-data-machine-learning
(Optional) Step 5a: If Your Focus Is NLP
DeepLearning.AI Natural Language Processing Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fnatural-language-processing%3F
Step 6: Master TensorFlow
Advanced TensorFlow Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Ftensorflow-advanced-techniques
BONUS
University of Alberta Reinforcement Learning: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement-learning%3F
DataBricks Apache Spark Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fdata-science-with-databricks-for-data-analysts
IBM Cloud / Web Development Specialization: https://click.linksynergy.com/deeplink?id=x*/jJiqX4sw&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Fprofessional-certificates%2Fibm-full-stack-cloud-developer%3Fpage%3D2%26index%3Dprod_all_products_term_optimization
My Step-By-Step PDF: https://drive.google.com/file/d/1zCNQV064hkBZLAOOJeDW2D4R8-gq6uxr/view?usp=sharing
#machine-learning #big-data
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In this session, you’ll get insights into how customers are using Microsoft Azure object storage offerings to scale their application environments and effectively manage huge quantities of unstructured data.
You’ll also find out how companies of all sizes are using Azure Blob Storage for data backup, archive, cloud-native apps, and high-scale workload scenarios—and how to use Azure Data Lake Storage as a scalable foundation for your analytics and big data workloads.
#developer #azure #big-data
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Discover, connect, and explore data in Azure Synapse Analytics using Azure Purview | Big Data| Data Governance
We will register an Azure Purview Account to a Synapse workspace. That connection allows you to discover Azure Purview assets and interact with them through Synapse capabilities.
You can perform the following tasks in Synapse:
Use the search box at the top to find Purview assets based on keywords
Understand the data based on metadata, lineage, annotations
Connect those data to your workspace with linked services or integration datasets
Analyze those datasets with Synapse Apache Spark, Synapse SQL, and Data Flow
#azure #big-data
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Everyone can profit from an open-source DocumentDB option with ACID properties for your landing data layer.
Hey guys, how are you guys doing? Today I want to present RavenDB, an open-source option for your OLTP systems of the document type. But before you roll your eyes, skeptic saying: “Yeah, my current choice does that, and even pours me a coffee.”
I want to reassure you that it’s not a long-term commitment here. And leveraging RavenDB as an option for your NoSql needs could grant you some perks. Such as its native support for OLAP modelling, integration with the major clouds and its ACID properties, to list some.
Sounds good? So if you have some spare time, I want to present to you what RavenDB does and why it can be a contender for your OLTP systems; we will then see how to query some data. We will wrap up with some use cases and considerations to have in mind for each of them.
RavenDB is a NoSql Document type database with exciting features, such as allowing your team to use SQL to explore your semi-structured data in the same way as you do for your structured data.
To understand how it helps, let’s do a quick recap on NoSql and Relational databases. Let’s start by remembering that our data is categorized by how its generated, and it can be in the following formats:
#nosql #big-data #microservices
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As organizations become proficient in capturing, storing, and analyzing data from multiple sources, they are discovering previously untapped business opportunities.
This has been possible with the help of Data Science which has been enabling the companies to make smarter, data-driven decisions, as well as build & deploy Big Data solutions faster. The challenge, however, is that the same services are not yet available at the mid-sized or smaller companies often due to the lack of Data Science professionals.
With graphical user interfaces and configuration, the Low-Code technology allows non-tech professionals to enter the world of development. They can build applications with no prerequisite knowledge of coding or other database management services. Gartner forecasts the global Low-Code Tech market to burgeon by 23% in the year 2021.
Low-code development platforms enable Data Science teams to derive analytical insights from Big Data quickly. With the co-existence of an array of features like Visual Modelling, Real-time monitoring & reporting, and Cross-platform accessibility among others, the low-code creates templates that replace any repetitive code structure, reducing the load from the algorithms.
This adds value to the work of developers and data scientists & accelerates the decision-making process. They can then focus on constructing information perceptions, structuring big data projects, or creating new products.
Leveraging Low Code for Big Data Analytics:
The data is still the data, but the ways of getting insights are continuing to improve. The use of Artificial Neural Networks like Machine learning in automating Big Data solutions has augmented exponential growth in the Digital economy. However, with a long & expensive deployment process, organizations are moving towards Low-Code programming for Big Data Analytics.
#low-code #low-code-platform #big-data #big-data-analytics
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Is data the new gold?
Considering the pace at which data is being used across the globe, definitely yes!
Let’s see some crazy stats.
To deter this problem, here is the list of 5 promising tips enterprises must acquire to turn their big data into a big success.
#big-data #big-data-analytics #big-data-processing #datascience #data analytics
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This cheat sheet helps you to choose the proper estimate for the task that is the hardest portion of the work. With modern computer technology, today’s machine learning isn’t like machine learning from the past.
The notion that computer may learn without being trained to do certain tasks came from pattern recognition researchers interested in artificial intelligence sought to explore if computers could learn from the information.
The iterative component of machine education is crucial because they may adjust autonomously when models are exposed to fresh data. From past calculations, they learn to create dependable, repeatable judgments and results. It’s not a new science, but a new one.
The usage of programming and even equipment is automation for computerized commands. AI, again, is the robots’ ability to reproduce human habits and thinking and get more clever all the time. It is important, while a misleadingly sharp computer may learn and modify its job as it receives new information, it cannot completely replace people. Everything is equal, it’s a resource, not a risk.
#artificial-intelligence #machine-learning #deep-learning #big-data #deep learning #machine learning
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#python #data #data-science #pandas #big-data
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A decade on, big data challenges remain overwhelming for most organizations.
Since ‘big data’ was formally defined and called the next game-changer in 2001, investments in big data solutions have become nearly universal.
However, only half of companies can boast that their decision-making is driven by data, according to a recent survey from Capgemini Research Institute. Fewer yet, 43%, say that they have been able to monetize their data through products and services.
So far, big data has fulfilled its big promise only for a fraction of adopters — data masters.
They are reporting a 70% higher revenue per employee, 22% higher profitability, and the benefits sought after by the rest of the cohort, such as cost cuts, operational improvements, and customer engagement.
What are the big data roadblocks that hold back others from extracting impactful insights from tons and tons of information they’ve been collecting so diligently?
Let’s explore.
#big-data #technology #data-science #big data problems
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Big Data and Data Science are real buzzwords at the present time. However, what are the differences between both terms and how are the fields related to each other? Can they even be considered as competitors?
Big Data refers to large amounts of data from areas such as the internet, mobile telephony, the financial industry, the energy sector, healthcare etc… Big Data can also extract figure sets from sources such as intelligent agents, social media, smart metering systems, vehicles etc. which are stored, processed and evaluated by using special solutions [1].
Data Science is about to generate knowledge from data in order to optimize corporate management or support decision-making. Methods and knowledge from various fields such as mathematics, statistics, stochastics, computer science and industry know-how can be therefore used here [2].
Unlike other trends, these two areas are not in competition but empower, or enable each other. New big data technologies have made it possible to analyze large amounts of data with data science tools.
Some examples of this are:
So you can see that Big Data makes many of the Data Science trends possible. Of course, data analytics can also take place without modern, cloud-based Big Data technologies, but due to the rapidly growing data volumes, these are increasingly becoming a prerequisite. Once the solid architecture is implemented, there are no limits for the data scientist and analyst. They can then run their analyses without technical limitations and mostly on their own.
#data-science #data-analysis #big-data