Hire the best
Inform Engineers

Top 30 Inform Engineers on 21 Apr 2019 on Toogit. Inform Engineers on Toogit are highly skilled and talented. Hiring Inform Engineers is quite affordable as compared to a full-time employee and you can save upto 50% in business cost by hiring Inform Engineers. Hiring Inform Engineers is 100% safe as the money is released to the Inform Engineers only after you are 100% satisfied with the work.

Get Started

Explore Toogit’s top Inform Engineers

 
 
 
Senior Professional
Sathya

Senior Professional  


Informatica Microsoft Excel Microsoft SQL Server 
$10 /hr
India
Technical Lead Real Time Banking Fraud Management Product Implementation Consultant and ETL Specialist
Sameer Prasad

Technical Lead Real Time Banking Fraud Management Product Implementation Consultant and ETL Specialist  


Informatica Data Migration Oracle Database 
$25 /hr
India
Business analysts
Rajesh Raju

Business analysts  


Informatica "Extract, Transform and Load (ETL)" Data Extraction 
/hr
India
Developer
Akash

Developer  


Informatica "Extract, Transform and Load (ETL)" Microsoft SQL Server 
$10 /hr
India
Project Tech Lead
Sourcing India

Project Tech Lead  


Informatica Oracle APEX BPO IT Services 
$5 /hr
India
Informatica developer
Suman Kishore Kumar

Informatica developer  


Informatica Oracle Database Oracle PLSQL 
$30 /hr
India
Workday Certified HCM Consultant, Compensation, Absence, data migration specialist, Workday integration.
Thejaswini

Workday Certified HCM Consultant, Compensation, Absence, data migration specialist, Workday integration.  


Informatica Data Migration Microsoft Excel 
$20 /hr
India
ETL Developer for data warehousing and data migration with 4 years of development experience in Finance and Pharma domain.
Biswajit

ETL Developer for data warehousing and data migration with 4 years of development experience in Finance and Pharma domain.  


Informatica "Extract, Transform and Load (ETL)" Data Warehousing 
$2 /hr
India
Senior ETL Developer
Priyanka

Senior ETL Developer  


Informatica Teradata Data Migration 
$27 /hr
India
ETL developer Salesforce ( gainsight developer)
Vamsi Krishna Sayarwar

ETL developer Salesforce ( gainsight developer)  


Informatica Salesforce Oracle PLSQL 
$40 /hr
India
Sr. Consultant
Vishnu Vardhan Reddy Akk...

Sr. Consultant  


Informatica Product Management Master Data Management (MDM) 
$17 /hr
India
Consultant
Amzed Khan

Consultant  


Informatica IBM InfoSphere DataStage Teradata 
/hr
India
DW/ETL/BI
Samson Isack

DW/ETL/BI  


Informatica Oracle Database Business Intelligence 
$33 /hr
India
IT Analyst
Suryakanta Prusty

IT Analyst  


Informatica Hive SQL Programming 
$10 /hr
India
Associate consultant
Srikanth Wits

Associate consultant  


Informatica Oracle Database "Extract, Transform and Load (ETL)" 
$0 /hr
India
Data Analyzer and Researcher
Gunasekaran

Data Analyzer and Researcher  


Informatica Pure Data Oracle Database 
$12 /hr
India
Software engineer
Vishnu

Software engineer  


Informatica Tableau Software Data Modeling 
$14 /hr
India
ETL and Big Data Resource
Niharika Rupainwar

ETL and Big Data Resource  


Informatica Hadoop 
$100 /hr
India
Informatica developer
Puneet Garg

Informatica developer  


Informatica sql 
$40 /hr
India
Data Analyst
Ankit Dhingra

Data Analyst  


Informatica Oracle Database Oracle PLSQL 
$8 /hr
India
ETL developer
Jeffrey Maenetja

ETL developer  


Informatica Pentaho Oracle Database 
$60 /hr
South Africa
Designer & Developer
Lokhapriya

Designer & Developer  


Informatica Oracle Database Unix 
$10 /hr
India
Application Developer
Shubham Agarwal

Application Developer  


Informatica Oracle PLSQL Software Architecture 
$10 /hr
India
Developer and Designer
Madhu

Developer and Designer  


Informatica Oracle Database Data Entry 
/hr
India
ETL developer
Harleen Kaur

ETL developer  


Informatica Teradata ETL 
$2 /hr
India
Data Integration Expert and Analyst
Kumar Patil

Data Integration Expert and Analyst  


Informatica Oracle Database Data Science & Analytics 
$20 /hr
India
Teradata DWH Consultant
Muhammad Imran

Teradata DWH Consultant  


Informatica Teradata ETL 
$36 /hr
Pakistan
Presales Manager
Madhavi Kurra

Presales Manager  


Informatica BigData SEO 
$12 /hr
India
BI Professional
Pratik Rudra

BI Professional  


Informatica Teradata MicroStrategy 
$34 /hr
India
Sign-up
to view more profiles

Get Started
 

How it works

Post a job

Post a Job

List your project requirement with us. Anything you want to get developed or want to add to your business. Toogit connects you to Top freelancers around the world.

Hire

Hire

Invite and interview your preferred talent to get work done. Toogit Instant Connect helps you if you need your project started immediately.

Work

Work

Define Tasks, use Toogit's powerful project management tool, stay updated with real time activity logs

Payment

Pay

Review work, track working hours. Pay freelancers only if you are 100% satisfied with the work done.

Popular How-To's in Inform category


 
How to migrate wordpress website files and databas...
Web Development

Moving websites between hosts is a big challenge for all site owners. With a WordPress site, we have to move all our plugins, themes, and the database. Once they are relocated, the...

Read More

Reviews From Our Users

Articles Related To Inform


What is a web scraping?

Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol, or through a web browser. While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the web, typically into a central local database or spreadsheet, for later retrieval or analysis.

Web scraping a web page involves fetching it and extracting from it. Fetching is the downloading of a page (which a browser does when you view the page). Therefore, web crawling is a main component of web scraping, to fetch pages for later processing. Once fetched, then extraction can take place. The content of a page may be parsed, searched, reformatted, its data copied into a spreadsheet, and so on. Web scrapers typically take something out of a page, to make use of it for another purpose somewhere else. An example would be to find and copy names and phone numbers, or companies and their URLs, to a list (contact scraping).

 

What you can do with data scraping?

Web scraping is used for content scraping, and as a component of applications used for web indexing, web mining and data mining, online price change monitoring and price comparison, product review scraping (to watch the competition), gathering real estate listings, weather data monitoring, website change detection, research, tracking online presence and reputation, web mashup and, web data integration.

Using data scraping you can build sitemaps that will navigate the site and extract the data. Using different type selectors you will navigate the site and extract multiple types of data - text, tables, images, links and more.

 

What role scraper should play for you?

Web scraping is the process of automatically mining data or collecting information from the World Wide Web. It is a field with active developments sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, semantic understanding, artificial intelligence and human-computer interactions. Current web scraping solutions range from the ad-hoc, requiring human effort, to fully automated systems that are able to convert entire web sites into structured information, with limitations.

 

Below are the ways for scraping data:

  • Human Copy Paste : Sometimes even the best web-scraping technology cannot replace a human’s manual examination and copy-and-paste, and sometimes this may be the only workable solution when the websites for scraping explicitly set up barriers to prevent machine automation.
  • Text Pattern Matching : A simple yet powerful approach to extract information from web pages can be based on the UNIX grep command or regular expression-matching facilities of programming languages
  • HTTP programming : Static and dynamic web pages can be retrieved by posting HTTP requests to the remote web server using socket programming.
  • HTML parsing : Many websites have large collections of pages generated dynamically from an underlying structured source like a database. Data of the same category are typically encoded into similar pages by a common script or template. In data mining, a program that detects such templates in a particular information source, extracts its content and translates it into a relational form, is called a wrapper. Wrapper generation algorithms assume that input pages of a wrapper induction system conform to a common template and that they can be easily identified in terms of a URL common scheme.Moreover, some semi-structured data query languages, such as Xquery and the HTQL, can be used to parse HTML pages and to retrieve and transform page content.
  • DOM parsing: By embedding a full-fledged web browser, such as the Internet Explorer or the Mozilla browser control, programs can retrieve the dynamic content generated by client-side scripts. These browser controls also parse web pages into a DOM tree, based on which programs can retrieve parts of the pages.
  • Vertical aggregation : There are several companies that have developed vertical specific harvesting platforms. These platforms create and monitor a multitude of “bots” for specific verticals with no "man in the loop" (no direct human involvement), and no work related to a specific target site. The preparation involves establishing the knowledge base for the entire vertical and then the platform creates the bots automatically. The platform's robustness is measured by the quality of the information it retrieves (usually number of fields) and its scalability (how quick it can scale up to hundreds or thousands of sites). This scalability is mostly used to target the Long Tail of sites that common aggregators find complicated or too labor-intensive to harvest content from.
  • Semantic annotation recognizing : The pages being scraped may embrace metadata or semantic markups and annotations, which can be used to locate specific data snippets. If the annotations are embedded in the pages, as Microformat does, this technique can be viewed as a special case of DOM parsing. In another case, the annotations, organized into a semantic layer,are stored and managed separately from the web pages, so the scrapers can retrieve data schema and instructions from this layer before scraping the pages.
  • Computer vision web-page analysis : There are efforts using machine learning and computer vision that attempt to identify and extract information from web pages by interpreting pages visually as a human being would.

 

Key Features of Web Scraping

In order to remain competitive, businesses must be able to act quickly and assuredly in the markets. Web Scraping plays a big role in the development of various business organizations that use the services. 

The benefits of these services are: 

  1. Low Cost: Web Scraping service saves hundreds of thousands of man-hours and money as the use of scraping service completely avoids manual work.
  2. Less Time: Scraping solution not only helps to lower the cost, it also reduces the time involved in data extraction task. This tool ensures and gathers fast results required by people.
  3. Accurate Results: Web Scraping solutions help to get the most accurate and fast results that cannot be collected by human beings. It generates correct product pricing data, sales leads, duplication of online database, captures real estate data, financial data, job postings, auction information and many more.
  4. Time to Market Advantage: Fast and accurate results help businesses to save time, money and labor and get an obvious time-tomarket advantage over the competitors.
  5. High Quality: A Web Scraping solution provides access to clean, structured and high quality data through scraping APIs so that the fresh data can be integrated into the systems.

Finding and hiring expert scraper/crawler

It’s important to note that not all scraper will be ideal fits for every project. For example, those with highly analytical backgrounds in software engineering would be ideal for developing algorithms but may not be the right fit for a data scraping project. That’s why it’s so important to understand what type of scraping expert will bring the most benefit to your company and business goals.

Here are some questions to consider:

What is the overall learning you hope to find? 

By including your goal in the project description, it allows professionals to better understand what type of work is required.

 

What core skills will scraping experts need to complete the project? 

The answer will revolve around your current data infrastructure and the processes used to extract information.

 

Would you benefit from someone with highly specialized skills in a few areas of data scraping, or would a well-rounded expert serve you better?

 

Are there any time constraints to consider with this project?

Let professionals know the amount of hours of work that might be involved.

 

What kind of budget will this project have? 

The more experience and expertise a data scraper has, the higher they expect to be compensated. Higher budgets will more likely give top-tier experts a reason to submit a proposal.

 

Web scraping project template

Below is a sample of how a project description may look. Keep in mind that many people use the term “job description,” but a full job description is only needed for employees. When engaging a freelancer as an independent contractor, you typically just need a statement of work, job post, or any other document that describes the work to be done.

<Job/Project Title>

ABC Company is looking for a web scraping expert to help us study our website traffic patterns and find areas of improvement. This project is estimated to require approximately 20-25 hours per week for the next few months to achieve the following goals

  • Reporting findings in a weekly summary
  • Split testing underperforming pages and recording results
  • Discovering which pages currently perform best
  • Organizing site data into spreadsheets

The following skills are required:

The ideal freelancer will be a creative problem solver with an excellent work history on Toogit. To submit a proposal, please send a short summary of similar projects you’ve completed and why we should consider you for this project.

  • Excellent technical abilities
  • Knowledge of quantitative split testing
  • Experience with WordPress and Google Analytics
  • A thorough understanding of MySQL databases
  • Expertise or extensive experience with Python

 

Hiring the right Web Scraping talent

Remember that technical ability is only a small portion of what makes an excellent web scraper. Great web scrapers are inquisitive—they want to ensure that they’re seeking the right types of answers, plus they’ll take an interest in your business to better understand it. The ideal professional will also be able to advise you on additional metrics to analyze and compare in order to help you meet your goals.

Also, keep in mind that communication is always a key consideration in the data science field. A brief interview can allow you to gauge how strong each professional is in expressing ideas and explaining their process. The more you speak to each professional by phone, email, or chat, the better you’ll be able to gauge their professionalism and communication skills and determine whether they’re right for your project.

Whenever there is a discussion regarding storing information on a 3rd party's database system, questions on security follow. Entrusting another company to stage your valuable information safe is a massive step. Once that information is in your control, you are aware of the protection measures in place to keep it safe.

 

Google assures users that it keeps all information safe and personal unless the user chooses to share files with others. As a part of its security measures, Google does not discuss its approach to security very well. Since users should have a Google account to access Google Docs, and since all accounts need passwords, we all know that at least one stage in Google's security plan depends on password protection.

 

Google Docs is the free data processing software that comes with a Google account. It’s designed to be easy to use. It can be used to create documents with rich formatting, images, and tables and features like footnotes, headers and footers, and page numbering. You can create your documents more engaging with pictures, drawing objects, and tables in Google docs.

 

Why Google Docs is the best way to create blog

If you're a professional blogger, all that you write must obviously be a result of your thorough research and will basically involve hard work. Whether it's Blogspot or WordPress, text editors of each of those blogging platforms are up to notch. Each text editors not only automatically save the post you are writing but also provide sufficient resources for content data formatting that helps you present well your content. Google Docs offers you the easiest and simplest way to format your content, provide blog templates, share it with collaborators, and even upload immediately to whichever CMS you use.

 

Integrate google keeps with google docs

Google Keep has officially been labelled as a part of the Google Suite of tools. It’s currently very easy to keep notes for a document you're working on. Along with the Explore feature, Google Docs has become a seriously impressive tool for business, education, and just about the other purpose that requires note keeping as you write. Google docs provide a tool to integrate google keep notes into document.

 

Migrate google docs to Microsoft word

Google Docs are in a web format, we can’t simply import them into Word! To open Google Docs in Microsoft Word, we need to need to convert Google Docs to Word’s DOCX format, then transfer it afterward. You can easily perform this conversion from Google Docs.

 

Google Docs has been around for a little while now. Businesses are adopting the tool as the way to extend efficiency and usability of information. I have yet to work for a business that actively uses Google Docs on a day to day, however I will definitely see the benefits of google docs.

  1. Accessibility: With Google Docs, staff can access the information 24/7 where they have an internet connection. This kind of flexibility is very useful, particularly for workers who are typically travelling and working from mobile devices.
  2. Version Control: Collaboration have a lot of importance within the workplace. Being able to not only access information from anyplace, but to be able to control the version of any document your staff are working on is a huge asset to your company. Google Docs permits you to add and take away collaborators. You can control exactly who can make changes to the document. In addition, multiple users can access and edit the same document at the same time.
  3. Easy to Learn: Google Docs is very straightforward and easy to pick up. If you have any experience with a word processor or programs such as Word, Excel, etc.
  4. Import/Export Flexibility: Google Docs imports and exports most file types, giving you the flexibility, you need when sending and receiving files from colleagues.

 

Hire Google Docs experts on Toogit.

NLP is a branch of data science that consists of systematic processes for analyzing, understanding, and deriving information from the text information in a smart and efficient manner. By utilizing NLP and its parts, one can organize the massive chunks of text information, perform various automated tasks and solve a wide range of issues like – automatic summarization, machine translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation etc.

 

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to lexical resources like WordNet, along with a collection of text processing libraries for classification, tokenization, stemming, and tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.

 

NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

 

Downloading and installing NLTK

  1. Install NLTK: run pip install nltk
  2. Test installation: run python then type import nltk and run nltk.download() and download all packages.

 

Pre-Processing with NLTK

The main issue with text data is that it's all in text format. However, the Machine learning algorithms need some variety of numerical feature vector so as to perform the task. Thus before we have a tendency to begin with any NLP project we'd like to pre-process it to form it ideal for working. Basic text pre-processing includes:

 

  • Converting the whole text into uppercase or lowercase, in order that the algorithm doesn't treat the same words completely different in several cases.
  • Tokenization: Process of converting the normal text strings into a list of tokens i.e. words that we actually want. The NLTK data package includes a pre-trained Punkt tokenizer for English.

 

           import nltk

           from nltk.tokenize import word_tokenize

           text = "God is Great! I won a lottery."

           print(word_tokenize(text))

           Output: ['God', 'is', 'Great', '!', 'I', 'won', 'a', 'lottery', '.']

 

  • Noise removal: Process of removing everything that isn’t in a standard number or letter.
  • Stop word removal: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”). We would not want these words or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to be stop words. NLTK (Natural Language Toolkit) in python has a list of stopwords stored in sixteen different languages. You can find them in the nltk_data directory.  home/Saad/nltk_data/corpora/stopwords is the directory address.

           import nltk

           from nltk.corpus import stopwords

           set(stopwords.words('english'))

 

  • Stemming: Stemming is the process of reducing the words to its root form. Example if we were to stem the following words: “Connects”, “Connecting”, “Connected”, “and Connection”, the result would be a single word “Connect”.

           # import these modules

           from nltk.stem import PorterStemmer

           from nltk.tokenize import word_tokenize   

           ps = PorterStemmer()  

           # choose some words to be stemmed

           words = ["Connect", "Connects", “Connected”, "Connecting", "Connection", "Connections"]

 

           for w in words:

           print(w, " : ", ps.stem(w)) 

 

  • Lemmatization: Lemmatization is the process of grouping along the various inflected forms of a word in order that they may be analyzed as a single item. Lemmatization is similar to stemming but it brings context to the words. Therefore it links words with similar meaning to one word.

           # import these modules

           from nltk.stem import WordNetLemmatizer  

           lemmatizer = WordNetLemmatizer()  

           print("rocks :", lemmatizer.lemmatize("rocks"))

           print("corpora :", lemmatizer.lemmatize("corpora"))  

           # a denotes adjective in "pos"

          print("better :", lemmatizer.lemmatize("better", pos ="a"))

 

          -> rocks : rock

          -> corpora : corpus

          -> better : good

 

Now we need to transform text into a meaningful vector array. This vector array is a representation of text that describes the occurrence of words within a document. For example, if our dictionary contains the words {Learning, is, the, not, great}, and we want to vectorize the text “Learning is great”, we would have the following vector: (1, 1, 0, 0, 1). A problem is that extremely frequent words begin to dominate within the document (e.g. larger score), however might not contain as much informational content. Also, it will offer additional weight to longer documents than shorter documents.

 

One approach is to rescale the frequency of words or the scores for frequent words called Term Frequency-Inverse Document Frequency.

 

  • Term Frequency: is a scoring of the frequency of the word in the current document.

           TF = (Number of times term t appears in a document)/ (Number of terms in the document)

 

  • Inverse Document Frequency: It is a scoring of how rare the word is across documents.

           IDF = 1+log(N/n), where, N is the number of documents and n is the number of documents a term t has appeared in.

 

           Tf-idf weight is a weight often used in information retrieval and text mining.

           Tf-IDF can be implemented in scikit learn as:

 

           from sklearn.feature_extraction.text import TfidfVectorizer

           corpus = [

           ...     'This is the first document.’

           ...     'This document is the second document.’

           ...     'And this is the third one.’

           ...     'Is this the first document?',]

           >>> vectorizer = TfidfVectorizer()

           >>> X = vectorizer.fit_transform(corpus)

           >>> print(vectorizer.get_feature_names())

           ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']

           >>> print(X.shape)

           (4, 9)

 

  • Cosine similarity: TF-IDF is a transformation applied to texts to get two real-valued vectors in vector space. We can then obtain the Cosine similarity of any pair of vectors by taking their dot product and dividing that by the product of their norms. That yields the cosine of the angle between the vectors. Cosine similarity is a measure of similarity between two non-zero vectors.

           Cosine Similarity (d1, d2) =  Dot product(d1, d2) / ||d1|| * ||d2||

 

          import numpy as np

          from sklearn.metrics.pairwise import cosine_similarity

          # vectors

          a = np.array([1,2,3])

          b = np.array([1,1,4])

          # manually compute cosine similarity

          dot = np.dot(a, b)

          norma = np.linalg.norm(a)

          normb = np.linalg.norm(b)

          cos = dot / (norma * normb)

 

After completion of cosine similarity matric we perform algorithmic operation on it for Document similarity calculation, sentiment analysis, topic segmentation etc.

 

I have done my best to make the article simple and interesting for you, hope you found it useful and interesting too.

Articles Related To Inform


How to write/compose a Job description for web scraping to achieve your goal with minimal line of code?
How to write/compose a Job description for web scr...
Data Extraction / ETL

What is a web scraping?Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites. Web scraping software may access the World Wide...

Read More
Google Docs: Impressive Tool for Business
Google Docs: Impressive Tool for Business
Web Content

Whenever there is a discussion regarding storing information on a 3rd party's database system, questions on security follow. Entrusting another company to stage your valuable infor...

Read More
Natural Language Processing in Python
Natural Language Processing in Python
Web Development

NLP is a branch of data science that consists of systematic processes for analyzing, understanding, and deriving information from the text information in a smart and efficient mann...

Read More

Other Freelancers In Similar Categories

Anil Kumar


I have 9 years of exp in us healthcare process I have worked with multiple organisations (Ar follow up, Eligibility...

Sagar Yende


An Interaction Designer with a strong affinity for Usability. Believes that a well-designed product is one that is...

Nidhi


I am MBA graduate with 4.5 years of experience as a Functional system analyst and 9 months of experience as an Info...

Ankush Gaikwad


CYBRAIN Infosec(Academy For Future Technology) is the best place to invest in Cyber Security and Ethical hacking Tr...

What our users are discussing about Inform