Author: DataOpsZone

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A Coder Guide to Data Science?

Data Scientist DataOpsZone

Data Science is an interdisciplinary field that utilizes mathematics, statistics, and computer science to extract meaningful insights from large datasets. It can be used to uncover patterns and solve complex problems in a variety of industries such as healthcare, finance, marketing, and engineering.

 Choosing the right language for a data science project is essential, and there are a variety of languages to choose from. Python, R, SQL, MATLAB, and Scala are some of the best languages for data science, each offering unique features and capabilities that make them suitable for different tasks.

Lets talk about the top 5 languages n more detail.

The What & When of:

  • 1. Python
  • 2. R
  • 3. SQL
  • 4. MATLAB
  • 5. Scala

Python

What is Python?

Python is a high-level, general-purpose programming language that is popular among data scientists for its flexibility, wide range of libraries, and ease of use. Python is used for data analysis, machine learning, web development, and more. It is a great language for beginners as it has a simple syntax and provides a wide range of libraries and modules to help with data manipulation and analysis.

When to choose Python?

Python is a great choice for data science projects that require a lot of data manipulation and analysis. It is also a great choice for projects that have a large and diverse dataset, as its wide range of libraries and modules will make it easier to process and visualize the data. Python is also a great choice for beginners, as it is easy to learn and provides a wide range of resources to help with data analysis.

R

What is R?

R is a programming language and software environment for statistical computing and graphics. It is popular among data scientists for its powerful statistical analysis capabilities and its wide range of libraries for data manipulation and visualization. R is particularly popular among academics and researchers, who use it to analyze data and build predictive models.

When to use R?

R is a great choice for data science projects that require a lot of statistical analysis. It is also a great choice for projects that require powerful data manipulation and visualization capabilities. R is popular among academics and researchers, so it is a great choice for projects involving research or analysis.

SQL

What is SQL?

SQL (Structured Query Language) is a domain-specific language used to interact with databases. It is used to store, retrieve, manipulate, and analyze data stored in a relational database. SQL is popular among data scientists to access and analyze data stored in relational databases, as it is easy to learn and offers powerful features for data analysis.

When to use SQL?

SQL is a great choice for data science projects that involve accessing and analyzing data stored in a relational database. It is also a great choice for projects that require a lot of data manipulation, as SQL offers powerful features for data analysis. SQL is also easy to learn, making it a great choice for beginners.

MATLAB

What is MATLAB?

MATLAB (Matrix Laboratory) is a high-level programming language and environment used for technical computing and data analysis. It is popular among data scientists for its powerful numerical computing and visualization capabilities. MATLAB also has a wide range of libraries for data analysis and machine learning, making it a great choice for data scientists.

When to use MATLAB?

MATLAB is a great choice for data science projects that require a lot of technical computing and visualization. It is also a great choice for projects that require a lot of data manipulation and analysis, as it has a wide range of libraries for data analysis and machine learning. MATLAB is also a great choice for projects involving numerical computing, as it has powerful numerical computing capabilities.

Scala

What is Scala?

Scala is a general-purpose programming language that is often used for data science projects. It is a combination of object-oriented and functional programming, and is popular for its powerful features and scalability. Scala is a great choice for data science projects, as it is easy to learn and offers a wide range of libraries for data manipulation and analysis.

When to use Scala?

Scala is a great choice for data science projects that require a lot of data manipulation and analysis. It is also a great choice for projects that require scalability, as it offers powerful features for data manipulation and analysis. Scala is also a great choice for projects that require a lot of object-oriented programming, as it offers a combination of object-oriented and functional programming.

One Size Doesnt Fit All

In many cases, a hybrid approach is best for data science projects. This involves combining the best features of different languages and tools to create a powerful and flexible data science solution. For example, combining Python and R can provide the best of both worlds, with Python providing powerful data manipulation and visualization capabilities, and R providing powerful statistical analysis capabilities.

No matter what language or tools you use, the most important thing is to choose the right ones for your particular project. Finding the right combination of languages and tools to best suit your project can take some experimentation, but it is well worth the effort.

Author Jane Temov

Jane Temov is an IT Environments Evangelist at Enov8, specializing in IT and Test Environment Management, Release and Data Management product design & solutions.

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What is Data Cloning? A Beginners Guide

What is Data Cloning

Data Cloning, sometimes called Database Virtualization, is a method of snapshotting real data and creating tiny “fully functional” copies for the purpose of rapid provisioning into your Development & Test Environments.

The Cloning Workflow

There are four primary Steps

  1. Load / Ingest the Source Data
  2. Snapshot the Data
  3. Clone / Replicate the Data
  4. Provision of the Data to DevTest Environments

Under the Hood

Cloning is typically achieved/built using ZFS or HyperV technologies and allows you to move away from the traditional backup & restore methods, which can take hours.

By using ZFS or HyperV you can provision databases x100 quicker and x10 smaller.

What is ZFS?

  • ZFS is a file system that provides for data integrity and Snapshotting. It is available for most if not all major OS platforms.

What is HyperV?

  • HyperV is a Microsoft virtualization platform that can be used to create and manage virtual machines. It supports Snapshotting as well.

Problem Statement

Backups are often taken manually and can take hours or days to complete. This means that the data isn’t available for use during this time period, which can be problematic if you need access to your data immediately.

There is also a secondary issue with storage. A backup & restore is, by its nature, a 100% copy of the original source. So if you started with a 5 TB database and wanted x3 restores then you are up for another 15 TB in disk space.

What are the Benefits of Data Cloning?

Data cloning is the process of creating a copy, or snapshot, of data for backup, analysis, or engineering purposes. This can be done in real-time or as part of a scheduled routine. Data clones can be used to provision new databases and test changes to production systems without affecting the live dataset.

Advantages

– Clones can be used for development and testing without affecting production data

– Clones use little storage, on average about 40 MB, even if the source was 1 TB

– The Snapshot & Cloning process takes seconds, not hours

– You can restore a Clone to any point in time by bookmarking

– Simplifies your End to End Data Management

Disadvantages

– The underlying technology to achieve cloning can be complex.

However, there are various cool tools on the market that remove this complexity.

What Tools are available to support Data Cloning?

In addition to building your own from scratch, commercial cloning solutions include:

Each is powerful and has its own set of features and benefits. The key is to understand your data environment and what you’re trying to achieve before making that final decision.

Common Use Cases for Data Cloning

  • DevOps: Data cloning is the process of creating an exact copy of a dataset. This can be useful for several reasons, such as creating backups or replicating test data, into Test Environments, for development and testing purposes.
  • Cloud Migration: Data cloning provides a secure and efficient way to move TB-size datasets from on-premises to the cloud. This technology can create space-efficient data environments needed for testing and cutover rehearsal.
  • Platform Upgrades: A large majority of projects end up going over the set schedule and budget. The primary reason for this is because setting up and refreshing project environments is slow and complicated. Database virtualization can cut down on complexity, lower the total cost of ownership, and accelerate projects by delivering virtual data copies to platform teams more efficiently than legacy processes allow.
  • Analytics: Data clones can provide a space for designing queries and reports, as well as on-demand access to data across sources for BI projects that require data integration. This makes it easier to work with large amounts of data without damaging the original dataset.
  • Production Support: Data cloning can help teams identify and resolve production issues by providing complete virtual data environments. This allows for root cause analysis and validation of changes to ensure that they do not cause further problems.

To Conclude

Data cloning is the process of creating an exact copy of a dataset (database). This can be useful for many reasons, such as creating backups or replicating data for development and testing purposes. Data clones can be used to quickly provision new databases and test changes to production systems without affecting the live dataset.

This article provides a brief overview of data cloning, including its advantages, disadvantages, common use cases, and available tools. It is intended as a starting point for those who are new to the topic. Further research is recommended to identify the best solution for your specific needs. Thanks for reading!

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GDPR Software: 11 Options to Help You Comply in 2022

GDPR Software 11

Businesses today have an ever-growing list of privacy restrictions to deal with when collecting and managing data. One of the most notorious pieces of privacy legislation is the EU’s General Data Protection Regulation (GDPR), which became the law of the land in 2018 and carries stiff penalties for violators.

Suffice it to say that GDPR compliance can be challenging. This is largely due to its size and scope as well as its evolving nature. In order to meet GDPR requirements, many organizations are turning to purpose-built software solutions that are designed to be GDPR-compliant out of the box. 

Without a doubt, this is the fastest and safest way to use data and avoid regulatory complications for businesses that sell to customers who reside in the EU.

What Is the GDPR?

The GDPR is one of the most comprehensive and far-reaching global privacy protocols implemented to date. It replaced the EU’s Data Protection Act and is now the main data privacy law in the EU.

While the GDPR is extensive, it boils down to some basic foundational principles. At a high level, companies that handle data from consumers in the EU need to operate with lawfulness, fairness, and transparency. They also have to limit the data they collect and focus on data minimization, accuracy, integrity and confidentiality, and accountability, among other things. 

GDPR also grants users eight basic rights to personal data and privacy, including the right to data access, data consent, data deletion, data portability, processing restriction, notification, and rectification.

The GDPR applies to all kinds of personal data, ranging from health and biometric data to basic identity information like names, mailing addresses, and email addresses. GDPR also impacts any company that collects or processes the personal data of residents of the EU regardless of the organization’s location. It applies to large companies as well as to small businesses with 250 or more employees.

Violators of the GDPR may face penalties of up to €20 million (about $23 million) or up to 4 percent of annual worldwide turnover from the previous financial year, whichever is larger.

What Is GDPR compliance?

When an organization is GDPR-compliant, it means the company meets the law’s various requirements for handling personal data.

The list of requirements is extensive. Some of the most important points involve designating an EU representative, embracing an opt-in mode of data collection, establishing time limits for breach notifications, and responding to customer requests for personal data.

Top GDPR Software Solutions To Consider 

In light of the extensive nature of GDPR, it comes as no surprise that organizations are struggling to comply. According to one study, 85 percent of U.S. companies believe that GDPR compliance regulations put them at a disadvantage against their European competitors.

Even though complying with GDPR is proving to be difficult for global businesses, recent technology advancements make it easier. In fact, there are a variety of GDPR compliant tools on the market that can help streamline workflows and keep you out of trouble. 

1. PrivIQ (Formerly GDPR365)

PrivIQ offers a one-stop shop for GDPR compliance. This platform provides everything you need to know to understand your company’s risks and to manage data privacy. 

Some of PrivIQ’s top features include data mapping, access to privacy notice and governance documents, breach logging support, and graphical dashboard reports. 

2. Onspring

Onspring provides cutting-edge risk management software that simplifies workflows, improves transparency, and helps maintain GDPR compliance. 

This software is excellent for capturing and remediating risks as they appear, including financial, reputational, and third-party threats. The software also makes it possible to control access by user, role, and group.

3. SolarWinds Access Rights Manager

SolarWinds Access Rights Manager (ARM) gives you everything you need to manage access rights across your entire IT environment to ensure GDPR compliance. 

Of note, the GDPR requires detailed user access monitoring. This is especially important for users with sensitive data. ARM can produce custom Active Directory and Azure AD reports, providing instant visibility into what different users can access. 

4. LogicGate Risk Cloud

LogicGate Risk Cloud is a cloud-based platform with prebuilt applications that perform a variety of critical GDPR-related functions. 

For example, the platform automates and centralizes customer requests, investigates breaches, and communicates with supervisory authorities. Additionally, LogicGate ensures that third parties are managing personal data effectively. 

5. Netwrix Auditor

Netwrix Auditor can minimize risk during a data breach. The platform quarantines sensitive data, secures overexposed documents, and manages privilege attestations, among other things.

By using a solution like Netwrix Auditor, your team can promptly discover security threats. If a breach occurs, you can spend less time combing through systems and databases and put more effort into dealing with customers and strategizing on a fix. 

6. OneTrust

OneTrust helps companies enhance their privacy programs. The platform offers prebuilt workflows, templates, automation, and regulatory intelligence to help operationalize data and remain in compliance with GDPR.

On top of this, this platform provides transparency about online tracking and captures consent for tracking technologies, cookies, and marketing communications. It also helps maintain and distribute policies and notices.

7. Vigilant Software Compliance Manager

Vigilant Software Compliance Manager identifies legal and regulatory information security requirements for GDPR. 

Using this software, your company can understand the specific actions that it needs to take to comply with various information security laws. Compliance Manager provides effective dates, direct links to legislation, and implementation requirements. 

8. Boxcryptor

Boxcryptor delivers advanced data protection using state-of-the-art encryption, which is a fundamental part of GDPR. 

With the help of Boxcryptor, your company can ensure that all data receives adequate protection in the cloud. The software encrypts files end to end locally on user devices before they go to cloud storage, enabling strong access control.

9. Didomi

Didomi is a leading privacy and consent management platform. The company offers Didomi for Developers, a comprehensive platform that runs on open APIs and helps integrate customer consent into operations.

Didomi makes it easy to build customer permission into your technology, enabling you to simplify privacy protection and preference management.

Further, the platform provides legal and business teams with real-time consent and preference data for easy compliance reporting. It also enables teams to know when consent is required when collecting new data or using it for different purposes. This in turn reduces risk and lets teams operate with greater confidence.

10. Iubenda

Iubenda makes apps and websites legally compliant across multiple legislations and languages, and GDPR is a main focus. 

With Iubenda, you can access helpful services like a privacy and cookie policy generator, a terms and conditions generator, and a consent solution. The company also offers a cookie solution to manage consent preferences for GDPR and other similar regulations.

11. Enov8 Data Compliance Suite 

Enov8’s Data Compliance Suite uses automated intelligence to identify security exposures and address issues before they lead to major incidents. 

The platform gives IT teams clean production-like data for developing and testing platform changes, eliminating complex and time-consuming manual work. 

Simply put, Enov8 enables teams to work faster and with less risk while eliminating costly remediation efforts and compliance issues. 

GDPR Compliance Can Be a Breeze with Enov8

Achieving GDPR compliance doesn’t have to be a nightmare. With the right software in place, your team can continue developing and using data at a fast pace while avoiding costly fines and penalties. 

To learn how Enov8 can help your organization achieve and maintain GDPR compliance, take the platform for a spin.

Post Author

This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling.