1598697780
Chain Analysis: Tools for AML & Fraud Investigations
Outlier Canada hosted yet another fantastic panel, this time focusing on chain analysis and analysis tools used for AML and fraud investigations. Amber Scott, CEO, Founder, and Chief AML Ninja at Outlier Canada moderated the panel, featuring Dina Mainville of CipherTrace, Giles Dixon of Grant Thornton LLP, Andrew Koral of Blockchain Intelligence Group, and Adam Goldman of BitBuy.
Be sure to visit us over at blockgeeks.com for everything you need to know about blockchain, cryptocurrency, and so much more!
#data analysis
What is GEEK
Buddha Community
1604008800
Static Code Analysis: What It Is? How to Use It?
Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.
Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.
“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”
- J. Robert Oppenheimer
Outline
We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.
We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.
Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.
How does it all work?
Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:
As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:
Scanning
The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.
A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7, Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.
Python provides the tokenize module in its standard library to let you play around with tokens:
Python
1
import io
2
import tokenize
3
4
code = b"color = input('Enter your favourite color: ')"
5
6
for token in tokenize.tokenize(io.BytesIO(code).readline):
7
print(token)
Python
1
TokenInfo(type=62 (ENCODING), string='utf-8')
2
TokenInfo(type=1 (NAME), string='color')
3
TokenInfo(type=54 (OP), string='=')
4
TokenInfo(type=1 (NAME), string='input')
5
TokenInfo(type=54 (OP), string='(')
6
TokenInfo(type=3 (STRING), string="'Enter your favourite color: '")
7
TokenInfo(type=54 (OP), string=')')
8
TokenInfo(type=4 (NEWLINE), string='')
9
TokenInfo(type=0 (ENDMARKER), string='')
(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)
#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer
1598697780
Chain Analysis: Tools for AML & Fraud Investigations
Outlier Canada hosted yet another fantastic panel, this time focusing on chain analysis and analysis tools used for AML and fraud investigations. Amber Scott, CEO, Founder, and Chief AML Ninja at Outlier Canada moderated the panel, featuring Dina Mainville of CipherTrace, Giles Dixon of Grant Thornton LLP, Andrew Koral of Blockchain Intelligence Group, and Adam Goldman of BitBuy.
Be sure to visit us over at blockgeeks.com for everything you need to know about blockchain, cryptocurrency, and so much more!
#data analysis
1623856080
Streamline Your Data Analysis With Automated Business Analysis
Have you ever visited a restaurant or movie theatre, only to be asked to participate in a survey? What about providing your email address in exchange for coupons? Do you ever wonder why you get ads for something you just searched for online? It all comes down to data collection and analysis. Indeed, everywhere you look today, there’s some form of data to be collected and analyzed. As you navigate running your business, you’ll need to create a data analytics plan for yourself. Data helps you solve problems , find new customers, and re-assess your marketing strategies. Automated business analysis tools provide key insights into your data. Below are a few of the many valuable benefits of using such a system for your organization’s data analysis needs.
Workflow integration and AI capability
Pinpoint unexpected data changes
Understand customer behavior
Enhance marketing and ROI
…
#big data #latest news #data analysis #streamline your data analysis #automated business analysis #streamline your data analysis with automated business analysis
1598001060
50+ Useful DevOps Tools
The article comprises both very well established tools for those who are new to the DevOps methodology.
What Is DevOps?
The DevOps methodology, a software and team management approach defined by the portmanteau of Development and Operations, was first coined in 2009 and has since become a buzzword concept in the IT field.
DevOps has come to mean many things to each individual who uses the term as DevOps is not a singularly defined standard, software, or process but more of a culture. Gartner defines DevOps as:
“DevOps represents a change in IT culture, focusing on rapid IT service delivery through the adoption of agile, lean practices in the context of a system-oriented approach. DevOps emphasizes people (and culture), and seeks to improve collaboration between operations and development teams. DevOps implementations utilize technology — especially automation tools that can leverage an increasingly programmable and dynamic infrastructure from a life cycle perspective.”
As you can see from the above definition, DevOps is a multi-faceted approach to the Software Development Life Cycle (SDLC), but its main underlying strength is how it leverages technology and software to streamline this process. So with the right approach to DevOps, notably adopting its philosophies of co-operation and implementing the right tools, your business can increase deployment frequency by a factor of 30 and lead times by a factor of 8000 over traditional methods, according to a CapGemini survey.
The Right Tools for the Job
This list is designed to be as comprehensive as possible. The article comprises both very well established tools for those who are new to the DevOps methodology and those tools that are more recent releases to the market — either way, there is bound to be a tool on here that can be an asset for you and your business. For those who already live and breathe DevOps, we hope you find something that will assist you in your growing enterprise.
With such a litany of tools to choose from, there is no “right” answer to what tools you should adopt. No single tool will cover all your needs and will be deployed across a variety of development and Operational teams, so let’s break down what you need to consider before choosing what tool might work for you.
- Plan and collaborate: Before you even begin the SDLC, your business needs to have a cohesive idea of what tools they’ll need to implement across your teams. There are even DevOps tools that can assist you with this first crucial step.
- Build: Here you want tools that create identically provisioned environments. The last you need is to hear “But it works for me on my computer”
- Automation: This has quickly become a given in DevOps, but automation will always drastically increase production over manual methods.
- Continuous Integration: Tools need to provide constant and immediate feedback, several times a day but not all integrations are implemented equally, will the tool you select be right for the job?
- Deployment: Deployments need to be kept predictable, smooth, and reliable with minimal risks, automation will also play a big part in this process.
With all that in mind, I hope this selection of tools will aid you as your business continues to expand into the DevOps lifestyle.
Tools Categories List:
Continuous Integration and Delivery
Infrastructure As Code
1. AWS CloudFormation
AWS CloudFormation is an absolute must if you are currently working, or planning to work, in the AWS Cloud. CloudFormation allows you to model your AWS infrastructure and provision all your AWS resources swiftly and easily. All of this is done within a JSON or YAML template file and the service comes with a variety of automation features ensuring your deployments will be predictable, reliable, and manageable.
Link: https://aws.amazon.com/cloudformation/
2. Azure Resource Manager
Azure Resource Manager (ARM) is Microsoft’s answer to an all-encompassing IAC tool. With its ARM templates, described within JSON files, Azure Resource Manager will provision your infrastructure, handle dependencies, and declare multiple resources via a single template.
Link: https://azure.microsoft.com/en-us/features/resource-manager/
3. Google Cloud Deployment Manager
Much like the tools mentioned above, Google Cloud Deployment Manager is Google’s IAC tool for the Google Cloud Platform. This tool utilizes YAML for its config files and JINJA2 or PYTHON for its templates. Some of its notable features are synchronistic deployment and ‘preview’, allowing you an overhead view of changes before they are committed.
Link: https://cloud.google.com/deployment-manager/
4. Terraform
Terraform is brought to you by HashiCorp, the makers of Vault and Nomad. Terraform is vastly different from the above-mentioned tools in that it is not restricted to a specific cloud environment, this comes with increased benefits for tackling complex distributed applications without being tied to a single platform. And much like Google Cloud Deployment Manager, Terraform also has a preview feature.
Link: https://www.terraform.io/
5. Chef
Chef is an ideal choice for those who favor CI/CD. At its heart, Chef utilizes self-described recipes, templates, and cookbooks; a collection of ready-made templates. Cookbooks allow for consistent configuration even as your infrastructure rapidly scales. All of this is wrapped up in a beautiful Ruby-based DSL pie.
Link: https://www.chef.io/products/chef-infra/
#tools #devops #devops 2020 #tech tools #tool selection #tool comparison
1597848060
Top 12 Most Used Tools By Developers In 2020
rameworks and libraries can be said as the fundamental building blocks when developers build software or applications. These tools help in opting out the repetitive tasks as well as reduce the amount of code that the developers need to write for a particular software.
Recently, the Stack Overflow Developer Survey 2020 surveyed nearly 65,000 developers, where they voted their go-to tools and libraries. Here, we list down the top 12 frameworks and libraries from the survey that are most used by developers around the globe in 2020.
(The libraries are listed according to their number of Stars in GitHub)
1| TensorFlow
**GitHub Stars: **147k
Rank: 5
**About: **Originally developed by researchers of Google Brain team, TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art research in ML. It allows developers to easily build and deploy ML-powered applications.
Know more here.
2| Flutter
**GitHub Stars: **98.3k
**Rank: **9
About: Created by Google, Flutter is a free and open-source software development kit (SDK) which enables fast user experiences for mobile, web and desktop from a single codebase. The SDK works with existing code and is used by developers and organisations around the world.
#opinions #developer tools #frameworks #java tools #libraries #most used tools by developers #python tools