Big Data

Big Data

Big data is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with.
 iOS App Dev

iOS App Dev


The Ultimate Guide to Functional Programming for Big Data

Pure Functions and Lazy Evaluations — The Crux of Distributed Data Computations

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

The Ultimate Guide to Functional Programming for Big Data
 iOS App Dev

iOS App Dev


Big Data — Know Your Data

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.

  1. So the first step is to accurately identify the problem we want to target — What is the business problem? Why does the problem have to be addressed? Which value do we expect to get when we solve it?
  2. Second, we need to fix objectives — What happened in the past and why did it happen? What is happening now, and what should I do in the present? And what do we expect to do in the future, along with our expectations.
  3. Identify stake-holders — Technology team, Data scientists, Subject matter experts.

#big-data #data-engineering #data-science

Big Data — Know Your Data

How MongoDB Emerged As A Leading General-Purpose Data Platform In 12 Years

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.

#big-data #mongodb

How MongoDB Emerged As A Leading General-Purpose Data Platform In 12 Years
 iOS App Dev

iOS App Dev


The Big Impact of Big Data on Businesses Today

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:

  • Informed decision-making
  • Cost reduction
  • Better understanding of customers
  • New product development
  • Data monetization

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

The Big Impact of Big Data on Businesses Today

Cloudera Data Platform — A Strong Performer In Cloud Data Warehouse Category

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.

Read more:

#data #big-data #cloudcomputing

Cloudera Data Platform — A Strong Performer In Cloud Data Warehouse Category
Ned  McGlynn

Ned McGlynn


Coursera Machine Learning Certificate Roadmap ✅

Coursera Machine Learning Certificate Roadmap ✔️.

Step 1: Begin Your Data Science Journey
IBM Data Science Professional Certificate:*/jJiqX4sw&mid=40328&

Step 2: Machine Learning Fundamentals
Andrew Ng / Stanford University Course:*/jJiqX4sw&mid=40328&
University Of Washington ML Specialization:*/jJiqX4sw&mid=40328&

(Optional) Step 2a: If You Want To Learn ML Math
Imperial College London Math For Machine Learning:*/jJiqX4sw&mid=40328&

(Optional) Step 2b: If You Want To Master Programming
Stanford University Algorithms Specialization:*/jJiqX4sw&mid=40328&

Step 3: Deep Learning Fundamentals
Andrew Ng Deep Learning Specialization:*/jJiqX4sw&mid=40328&

Step 4: Apply Your ML Skills
TensorFlow Deep Learning Specialization:*/jJiqX4sw&mid=40328&

Step 5: Move To The Cloud
Amazon Web Services Data Science Specialization:*/jJiqX4sw&mid=40328&
Google Cloud Big Data & ML On GCP:*/jJiqX4sw&mid=40328&

(Optional) Step 5a: If Your Focus Is NLP
DeepLearning.AI Natural Language Processing Specialization:*/jJiqX4sw&mid=40328&

Step 6: Master TensorFlow
Advanced TensorFlow Specialization:*/jJiqX4sw&mid=40328&

University of Alberta Reinforcement Learning:*/jJiqX4sw&mid=40328&

DataBricks Apache Spark Specialization:*/jJiqX4sw&mid=40328&

IBM Cloud / Web Development Specialization:*/jJiqX4sw&mid=40328&

My Step-By-Step PDF:

#machine-learning #big-data

Coursera Machine Learning Certificate Roadmap ✅
Jaimin Bhavsar

Jaimin Bhavsar


Scale Your Globally Distributed Workloads with Object Storage

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

Scale Your Globally Distributed Workloads with Object Storage
Unique  Barrows

Unique Barrows


Discover, Connect, and Explore Data in Azure Synapse Analytics using Azure Purview

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

Discover, Connect, and Explore Data in Azure Synapse Analytics using Azure Purview
Nat  Grady

Nat Grady


Leveraging RavenDB As NoSql Option for Document Storage

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.

What is RavenDB

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:

  • Structured (Relational databases or delimited flat-files);
  • Semi-structured (nested structures separated by key-value pairs as shown below);
  • Non-Structured (multimedia content like Photos and Videos).

#nosql #big-data #microservices

Leveraging RavenDB As NoSql Option for Document Storage

Low-Code Development Helps Data Scientists Uncover Analytical Insights

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.

Enter Low-code development

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.

**How does this work for Data Scientists? **

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

Low-Code Development Helps Data Scientists Uncover Analytical Insights

Turn Big Data into a Big Success: 5 Tips for Effective Big Data Analytics

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.

  • Invest in Leadership
  • Skill Development
  • Perform Experimentation With Big Data Pilots
  • Focus on the Unstructured Data
  • Incorporate Operational Analytics Engines
  • Final Thoughts

#big-data #big-data-analytics #big-data-processing #datascience #data analytics

Turn Big Data into a Big Success: 5 Tips for Effective Big Data Analytics
Sofia  Maggio

Sofia Maggio


Cheat Sheets for Artificial Intelligence, Neural Networks, Machine Learning, Deep Learning

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.

Python for Data Science

A language of programming is a batch of instructions producing input, which is termed output productivity. Languages of programming are built on algorithms and establish a framework for maximizing access and progress. Essentially, apps, websites, and programs are valued for development. Python is the best language for Data Science and it has several syntactic words and conditions. Specific experiences include being a knowledgeable coder.
  • TensorFlow
  • Scikit-Learn
  • Keras
  • Numpy
  • Data Wrangling
  • Scipy
  • Matplotlib:
  • Data Visualization
  • PySpark
  • Big-O
  • Neural Networks

#artificial-intelligence #machine-learning #deep-learning #big-data #deep learning #machine learning

Cheat Sheets for Artificial Intelligence, Neural Networks, Machine Learning, Deep Learning

5 Big Data Problems and How to Solve Them

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 challenge 1:

  • Data silos and poor data quality

Big data challenge 2

  • Lack of coordination to steer big data/AI initiatives

Big data challenge 3:

  • Skills shortage

Big data challenge 4:

  • Solving the wrong problem

Big data challenge 5:

  • Dated data and inability to operationalize insights

#big-data #technology #data-science #big data problems

5 Big Data Problems and How to Solve Them

Big Data vs. Data Science

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?

Terms & Definitions

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].

Against each other or with each other?

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:

  • IOT: Only through Big Data, real-time systems can handle the flood of data and can both manage and prepare them for analysis.
  • ML: Analyses based on artificial intelligence require a lot of computing power, which is also only possible with modern Big Data cloud architectures.
  • Self Service BI: Hundreds of users building and sharing their own reports? In this case, a solid infrastructure is crucial here, to ensure a stable environment when working with large amounts of data.

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

Big Data vs. Data Science