Data Processing

Moving applications from one platform to another can be an urgent business need at times. It is also a nightmare for businesses and people struggling with Information technology platforms. When you plan to move an application, various complex processes run in the background. Now detaching those from one platform and shifting the entire process to another can be an extensive task that requires endless lines of coding. For this reason, businesses find this task exigent and stick to one platform, even after realizing that the other one might have huge potential for the application.

KIBA, on the other hand, is a lightweight ETL framework for Ruby. It eases the hefty task of data migration without having to spend huge loads of time or writing immense lines of code for a simple task. During the process of data migration, there is a lot of update and transformation required in the old system before it feeding into the new system. It is a complex process and instigates fear in the heart of business people responsible for the task. Even the simplest tasks of data migration can prove to be painful for an organization.

Understanding KIBA

To begin with KIBA, a simple migration of application may include extracting data from one source, transforming or processing it and then storing it at a new location. As complex as it sounds even for a basic data migration task, with KIBA this task could never have been easier. You might have heard about Ruby on the Rails. KIBA is just another framework of Ruby that runs on the ETL cycle. For those who are unaware about ETL, it is the abbreviation of

• E- Extract: Extract refers to extracting data from the source system for further processing. It is the first step in the cycle.
• T- Transform: This is the second step of the ETL cycle. It is the part of the cycle where you apply functions as per the requirements to transform the data into a standard schema. This stage helps in preparing the data for the destination or delivery to the final migration platform.
• L- Load: The last part of the cycle is called load. It is where the data is loaded into the data mart and consumed. All the processing and transformation occurs in this part of the process. After the transformation takes place in the data mart, it is passed onto the cube and reaches the destination. Here it can be then used for browsing.

Just the way it sounds, ETL is a substituted word for data migration and the quintessential framework built under Ruby on the Rails for this task. When talking about ETL, you are sure about two things- the source from where you pull the data and the destination, where you deploy it. The transformation process in the middle ensures that the source data matches the desirable format to fit into the new platform. To begin with, the three steps to swear in the process of data migration include:

• Pulling the data from the source
• Changing it to match requirements
• Writing it into the new destination

KIBA aims at solving this issue and making the entire process hassle-free. Various skeptical approaches to ETL processing have proved that KIBA stands as a robust solution with its simplicity and flexibility. ThibautBarrereis the father of KIBA that has ever since its birth been used extensively by organizations for simplifying their data migration processes.

Attractive Features of KIBA

Take-away pipeline

Transformation on KIBA has unique and useful features that are not just for a one-time utility, but used again and again as per the requirements of data migration. Understand it analogous to a pipeline. KIBA has some transformation pipes that you can take anywhere of your choice. Moreover, these transformations can be taken anywhere and tested separately. This practice will bring a fully mature development cycle to your data processing ETL.

Pre-processors

KIBA offers some attractive features like the pre-processors in ETL cycle. Pre-processors are run just before the sources and accessed in the pipeline. It helps in defining the data processing lifecycle. In technical terms, a pre-processor is a block that you call before reading the first row of your source. Pre-processors handle any overall setup required in the ETL cycle.

Post-processors

KIBA also offers post processors in the ETL cycle. These are run in the program when there is a need to write all the rows to the destination. It also helps in defining the pipeline. In technical terms, you can call a post processor a block after you migrate all the rows in data migration. Post-processors handle any overall cleanup required in the process.

Storing ETL jobs

There are different ways in which you can store ETL jobs in KIBA. It mostly depends on how you call them.

• In one instance you may use the command line to store EYL definitions with an extension .etl
• In another instance, you can call your KIBA jobs through programming via tools such as Rake or Sidekiq etc. You can store your ETL definitions in .rb extension for this process.

KIBA ETL Lifecycle

The ETL lifecycle in KIBA can be broken down for the simplification process and comprehensive understanding.

• A KIBA source is a special defined class that has a constructor specific to the source. For example, it could be a file name. It implements a keyword ‘each’ that yields rows to the block.
• To implement your source, you must create a Ruby class that specifically corresponds to ‘each’. It will help you yield all the rows one by one. This method includes one constructor and method.
• In the ETL declaration, you also need to pass the parameters of the class and constructor in KIBA. KIBA will automatically do the instantiation and creation of the source constructor.
• For example, if you want to read a CSV file source, KIBA will open the file by using headers to build a hash or hashes of data and close it at the end. In KIBA, you will be able to yield only one row per CSV file.
• Once you have your class, you can easily declare your source in the ETL KIBA script. After this, the arguments passed to the source by KIBA, and the source starts yielding one row after another to feed the ETL pipeline.
• The KIBA transform is just a class which corresponds to a process in the Ruby Rails. It simply receives the row at a definite stage of a pipeline, which you can modify as per the requirements. Once modified, you must return it to pass through the next stage of the pipeline. The syntax for returning is the same as passing. The process incorporates the use of the keyword ‘transform’ in the pipeline.
• The final stage of the ETL lifecycle is the destination. The destination in KIBA is a class which has a constructor similar to that of the source. It helps in the initialization of the destination in the ETL pipeline. It also takes on the responsibility to send the row to the target data destination store.

Two major methods in this class are:

• Write: It helps in writing the row to the destination.
• Close: It helps in cleaning up after itself. For example, closing a file or database connection.

Challenges

ETL with iteration

When there are large data sets involved, ETL gets slow. So in order to execute queries without any difficulty, the developers suggested the use of #start, #step, and #stop blocks to add iteration. This iteration will optimize the code block and ensure quick results.

ETL Dependencies

Ruby ETL has dependencies before it can query to all databases. ETL can make a connection if the object to #query. To fix this, a library is added to the code. Ruby’s Simple Delegator is added along with a #query method, in some cases.

ETL complexity

ETL gets complicated when there are multiple data sources. A complex environment which has to use data sources which are of type databases, flat files, excel docs and service logs, ETL can slow down the process. These complex scenarios are usually resorting to big data for a solution. All these dependencies and problems have led to the newer version of KIBA 2.0.

KIBA 2.0

The newer version of KIBA , introduced a new Streaming Runner engine. The opt-in engine is called the Streaming Runner. The engine allows to generate in-class transforms, an arbitrary number of rows. This engine can improve the reusability of the KIBA components. To the above-mentioned challenges, KIBA has come up with this reusable and reliable KIBA version.

Conclusion

Thus, the KIBA ETL processing offers a code-centric lifecycle. The facilitation of various tested components can further ease the task of data migration and processing in businesses. KIBA has been able to make its mark in the world of data processing because of its ease of use, powerful domain specific language or DSL. It has also filled the gap of the Ruby ETL ecosystem. With reusable and easy to maintain components, KIBA accounts for a widely used data enriching and transforming tool.

Data has been the core of businesses for a long time. It has helped organizations at various operational and decisive stages to overcome challenges and develop critical strategies. Hitherto, the techniques and processes for obtaining data were difficult. As a result, only a few business giants could obtain it. Data processing was a challenging task. Furthermore, also lacked the relevant interpretation that could be put to use by marketers. Market research was widely unknown to small and medium businesses. Moreover, only those organizations that were able to drive effective insights through data processing crushed the market competition.

Data Processing for contemporary businesses

In reference to modern times, businesses barely possess a lack of data. They are flooded with data. But they face trouble drawing effective insights out of it. Many businesses these days use data processing to support their decisions rather than to form them. Moreover, it is in one way, undermining the potential of data and restricting businesses to reach their full potential.

The power of data quite drove the Scottish writer and poet Andrew Lan. Furthermore, in the year 1910, he said that he similarly used the data as a blind man uses a lamp post. It was for support instead of illumination. Organizations now, do not uncover the complete potential of data processing and use it as the famous Andrew Lang.

Data processing has the immense power to drive business decisions and change their gameplay in the market. You might be sitting on a pile of public data. However, that is of no use to your business until you can drive insights out of it. Furthermore, translating raw data is a crucial factor in drawing insights and driving decisions for a business.

Today, businesses are using big data and analytics to translate raw data into insightful information. And, these tools are transforming the business and redesigning its core processes. Here are a few effective strategies that will help businesses draw effective insights with data processing-

Utilizing Data Processing for your business

Below is how to utilize data processing for your business.

Start by defining a plan

The first step while drawing insights with data processing is to construct a plan on the paper. Figure out a plan of action and mention all the key factors that you want from the translating data. Furthermore, write down the overall ideas for your company and suggest the desirable outcomes from them.

The next step is to ask the relevant questions. These questions should be measurable, concise, and clear. For example, if your business has a specific problem or situation, ask statistical questions that the concerned data can answer. Let’s say that your product reach has degraded in the market; you must ask questions such as:

• “Am I reaching out to all of my target customers?” or
• “Should I expand my demographics?” etc.

Know how to source your data: Set your basic plan or set of goals for your organization. Now, the next step is to break them down into relevant factors. These can be:

Deciding your measurable goals

To begin the conceptualization of data for your business, you must start by measuring your goals. In most situations, organizations are already sitting on the pile of data. But, they don’t know how to use it. You can use various approaches to understand and filter out goals that you need to answer through data processing. At this point, one must get as creative as one can to work on their measurable goals.

For example, both the manager and operational staff need to do their homework. It can be in the ways that they can utilize data effectively for their organization. On the one hand, the operational staff must understand how to use the weekly or monthly customer statistics. The managers must be creative about new sources of data and their relevant potential for the business.

To start with, you can use some practices to churn ideas about building an approach towards the data. These can be:

• Sorting: It refers to ranking the elements by their importance towards the business.
• Filtering: Filtering refers to narrowing down everything else only to the element of interest.
• Segmenting: This refers to segmenting or forming relevant chunks of customers based on their personas and other factors.
• Visualizing: It includes utilizing imagery and visuals to utilize the data.

Choosing tools to measure these goals

Choosing the relevant tools is one of the most crucial decisions in processing data for your business. You can use the necessary IT support to start with the foundation of translating data. It becomes quite difficult to make use of the unstructured data. Therefore, one must work on quickly identifying and working with the most interesting or relevant data for the business. Furthermore, a cleanup process that you can deploy can merge the rest of the data. It can then associate itself with useful pieces.

Choose tools to measure your goals or drive insights for your business. Before that, you must ask the relevant questions concerning this step. Not only does it help in forming more comprehensive and timely insights, but also backs them up. Points to keep in mind in this phase of data processing are:

• The timeframe for the insights
• Relevant costs
• Units of measure
• Factors to be included

Data collection and optimization

Data collection is a crucial step in driving insights for the business. It forms the basis of crafting insights that are crucial to businesses. On the other hand optimization of business processes ensures that they stay relevant towards the nature of data. Often people build business models that are not practical and optimized for the outcomes. Therefore, any business model that aims at data processing must start with identifying an opportunity or improvement goal in mind. It is also significant to remember that the model should not exhaust all of the company’s capabilities. Instead, it should be less complex and focus on improving the nature of business goals. When collecting data:

• It is important to determine the data that can be collected from the databases and then from external sources.
• A strong and understandable file naming convention must be adopted so that most of the members can easily collaborate.

Bring life to data

Unless you bring the data back to life, it can barely be used to fulfill the needs of businesses. Converting the data into a presentable form can help in deriving actionable information or insights. Graphical summaries can help in uncovering various pieces of information that can prove beneficial to the companies. The real power of interpretation of data lies in displaying unexpected findings in a clear and crisp manner. Furthermore, various tools and practices in the market can help in translating raw data into relevant insights.

Measures towards achieving Effective Insights through Data Processing

To invoke compelling insights for your business-

Follow trends

It is best advised to follow trends rather than particular insight points of data. It helps in finding out the changing patterns and most importantly a direction to which the statistics are pointing. This approach provides an in-depth understanding of the market.

Focus on aesthetics

Presenting the beautiful analytical charts opens the mind and presents various horizons for interpretation.

Take note of the time range

Time ranges manifest a trend in market analysis especially customer behavior, buying patterns, etc. You can use ranges to monitor trends such as over a week, month, quarter, etc.

Take a skeptical point of view

Monitor data from a skeptical point of view. Having just one angle towards data processing might leave you in a biased situation. For example, you can try plotting the same data over different platforms or in different forms. It will ensure a holistic view of the insights and a comprehensive understanding. Furthermore, you can present your story more accurately.

Deploy a team

Monitoring data must not be one man’s job. Make sure you have an appropriate team to monitor the data from different perspectives. It will help in generating relevancy and actionable insights.

Look for relationships

You can only draw effective insights if there are strong relationships. The most powerful insights demonstrate strong relationships between variables or correlations. It can be beneficial to statisticians and data interpreters.

Business growth with Data Processing

Data processing can help in business growth in a variety of ways. Recent market statistics suggest that businesses experienced as much as 30 percent growth with the assistance of an in-house data processing team.

• Better service-level performance: Data processing can assist in generating predictive behavioral analysis for improved functioning at the service levels.
• Cost reductions: Data processing measures can reduce business costs by analyzing staffing level predictions and defining the operation funnel accordingly.
• Improved management: Data processing can help in improved business management at various levels by generating thoughtful insights at various levels.

These can be at:

• Product management by providing insights into the right products to focus on at the right time.
• Supplier management by improving product quality perspective.

Conclusion

The entire framework for data processing is extensive and can help businesses reap huge benefits for their business. It can provide them with a niche in the market and propel organizations. Effective insights depend on the nature of translation and comprehensive representation of the data. It is not surprising to find its similarity to an architect’s blueprints. Furthermore, you are free to approach it from as many creative perspectives.

We all admire champions/saint who guides us through the difficult circumstances and the problems we thought are painful are really unimportant in nature. On the off chance that individuals can illuminate and convey at a substantially bigger scale, you can as well!

In the event that you thought learning information science is troublesome or profound neural nets isn’t some tea – admire the good examples who made them. Following these good examples gives you a day by day motivation, an inspiration to discover greater reason throughout everyday life and to accomplish it.

“Role models set goals for you and try to make you as good as they are. Role models are important”

Now, I’ll acquaint you with an association of extreme information researchers on the planet. I think these information researchers have not exclusively done magnificent work, they have all abandoned a heritage the work they have done. In this way, here is a little introduction and tribute to these good examples.

• These information science maestros have propelled and guided a great many competitors over the world.
• Including their uninhibitedly available web journals, instructional exercises, recordings and so on.
• They are the ones who imagined the term ‘information researcher’, ‘data science’.
• They have opened the puzzle behind profound learning and neural nets.

I have comprehensively grouped these good examples in three classifications. I realize that may sound senseless – a devotee characterizing the good examples, however, I have done it in the enthusiasm of earning the Data Science. Here are the classes I have sorted individuals in-

Research-based Data Scientists

Data Scientists who have been or have eventually turned into a Professor/analyst to center around development.

Information Scientists Turned Entrepreneurs

These are information researchers who have utilized information science to make items or administrations.

Information Scientists in real life

Obviously, this does not imply that the over 2 are not in real life. This basically alludes to the way that these information researchers are driving the activity in information science through their commitments.

I know a significant number of you would be quick to interface/take after with these information researchers. Subsequently, for your benefit, I have given the connections of their particular LinkedIn/Twitter profiles.

Here are the TOP 10 Data Scientists who are big Data lovers

You can also navigate to their LinkedIn accounts by just clicking on their names.

Research-based Data Scientists

1- Geoffrey Hinton

Bio– Have you heard of the term ‘Back Propagation’? He is the mind (co-designer) behind this calculation for preparing neural nets and profound learning recreations. Also, Geoff concocted the term ‘Dull Knowledge’ where a set of huge information is processed with no impact on the cost work utilized for preparing or on the test execution.

Current work – He is broadly known for his work on Artificial Neural Networks. In 2013, he joined Google and drove its AI group.

Education- Above all, Geoff holds a Ph.D. in Artificial Intelligence from Edinburgh. He and his exploration bunch have been the main impetus behind the resurgence of neural systems and profound learning.

2- Yann Lecun

Bio- Yann is as of now working at Facebook as Director of AI Research wing. He is the establishing chief of NYU Center for Data Science.

Current work- He has taken a shot at a few profound learning ventures and has 14 US licenses enlisted. He’s additionally connected with New York University as a Professor from the previous 12 years.

Education– Yann holds a Ph.D. in Computer Science from Université Pierre et Marie Curie (Paris VI). He is a master in Machine Learning, Statistical operations, Deep Learning and Computer Vision.

3- Yoshua Bengio

Bio – Youshua commitments to Deep Learning and Artificial Intelligence have him world’s consideration. Among his various achievements, he holds Canada Research Chair in Statistical Learning Algorithms, NSERC Chair and numerous other.

Current work – Yoshua is the Founder and R&D Guru of ApSTAT Technologies. He is likewise connected with Université de Montréal as a Professor from recent years. Beforehand, he worked with AT&T and MIT as a machine learning specialist.

Education- He holds Ph.D. in Computer Science from McGill University.

Information Scientists Turned Entrepreneurs

4- Andrew Ng

Bio- Andrew Ng helped to establish Coursera with Daphne Koller. Through Coursera, alongside different orders, he made the information science learning open to just free to learn! Most importantly, he’s broadly known for his Machine Learning Course.

Current work- He is as of now functioning as a Chief Scientist at Baidu where he’s associated with doing explores in Deep Learning and in versatile ways to deal with Big Data and AI.

Furthermore, he’s related with Stanford University as an Associate Professor. He’s additionally the Founder and Lead of Google’s Deep Learning Project.

Education– He holds a master degree in 1988 from Massachusetts Institute of technology in Cambridge Massachusetts. In 2002 a PhD from University of California.

5- Daphne Koller

Bio- Her advantage zones lie in Machine Learning, Artificial Intelligence, Pattern Recognition and so forth.

Current work- Daphne is the President and Co-Founder of Coursera. She filled in as a Professor at Stanford University for very nearly 18 years.

Education- She holds a Ph.D. degree in Computer Science from Stanford. Throughout the years, Daphne has been respected with different honors, for example, ONR Young Investigator Award, ACM Infosys Award, MacArthur Foundation Fellowship and so on.

6- Hilary Mason

Bio- Her advantage zones lie in Machine Learning, Data Mining, Python. She got included in the rundown of Fortune 40 under 40 ones to watch 2011, Craig’s 40 under Forty 2012. Alongside numerous different honors, she was the beneficiary of TechFellow Engineering Leadership Award 2012.

Current work-  In the first place, Hilary is the Founder of Fast Forward Labs. She additionally helped to establish hackNY.org and DataGotham. Although, she filled in as a Chief Scientist at Bitly and Assistant Professor at Johnson and Wales University.

Education- She is a BA graduate from Grinnell College in 2000 in Computer science.

One more in this category

7- Carla Gentry

Bio- Carla Gentry is a Data Scientist and Founder of Analytical Solution. She is a standout amongst the most prevalent identity in Big Data people group to take after on Twitter. In 2013, she was among the “10 IT Leaders to Follow on Twitter.”

Current work- Additionally, She is working with Analytical Solution as a Data scientist from past 7 years.

Education- She holds a graduate degree in Maths and Economics at the University of Tennessee. With this conveys a significant affair of more than 15 years which incorporates working for Fortune 500 organizations like Hershey, Kraft, Johnson and Johnson, Kellogg’s and Firestone.

Information Scientists in real life

8- DJ Patil

Bio- DJ Patil alongside Thomas.H.Davenport, composed the famous HBR article – Data Scientist: The Sexiest Job of 21st Century”. He was chosen as 2014 Young Global Leader by the World Economic Forum.

Current work- However, he is working as US Chief Data Scientist at the White House Office of Science and Technology. In Fact, he filled in as VP of RelateIQ and Head of Data Products and Chief Scientist at Linkedin. Numerous licenses have been documented under his name.\

Education- He holds a Ph.D. in connected arithmetic from the University of Maryland.

9- Monica Rogati

Bio- Her enthusiasm lies in transforming information into items, significant bits of knowledge and important stories. Her advantage territories lie in Machine Learning, Statistical Text Mining, and Recommender Systems and so on.

Current work- Monica is as of now filling in as Data Science Advisor with Insight Data Science. Beforehand, she worked with Linkedin as a Senior Data Scientist, and was Vice President of Jaw Bone and considered numerous more mindful positions.

Education- She holds a Ph.D. in Computer Science from Carnegie Mellon University.

10- Doug Cutting

Bio- He is called as the daddy of Hadoop. With his hard work and knowledge, he is a master in Apache, Nutch, Hadoop and Avro open source.

Current work– Before, he worked with top MNCs like Apple, Yahoo etc. From the past 14 years, he’s connected with Apache Software Foundation. He is Chief Architect with Cloudera.

Education- With this, he holds a bachelor degree from Stanford University.

Top 2 Indian Data Scientists

Ankur Narang

Bio- With a B.Tech. & Ph.D.  from IIT Delhi, he has 40+ publications in Computer Science & Machine Learning conferences and journals, along with 15 granted US patents. Moreover, he has held multiple Industrial Track and Workshop Chair positions and has given invited talks in multiple conferences. He led the design & implementation of AI-based cognitive workflows for inverse problems using oil & gas production data.

Current work- As a Senior Vice President at Yatra Online Pvt. Ltd. Dr. Narang heads Data Science and AI practice for decision science department. Also, he holds 24 years of experience working with top MNCs like Sun research, IBM in the same domain.

As Chief Data Scientist and AVP Data Science at Mobileum, heading the expansion of voice and data fraud expertise. Additionally, as CTO, he also developed game theory, ML, and revenue management for large Media & FMCG companies. Furthermore, at Yatra, he is working on AI-based approaches for marketing and discounting optimizations and personalized chatbot experience.

Education- B.Tech and PhD from IIT Delhi.

Nitin Sareen

Bio- Nitin is a solid devotee and professional of utilizing progressed investigation as a vital differentiator crosswise over areas and has a demonstrated reputation for making sway by tackling key business issues with insightful scientific critical thinking.

Current work- He right now drives the Data Science bunch at WalmartLabs to use enormous information, information science and innovation to empower quicker and more brilliant business choices. Besides this, he is driving key activities to convey algorithmic items that devour Walmart scale information and mix more quick-witted choices crosswise over retail lifecycle like site determination, combination advancement, estimating, request anticipating, inventory network, store operations and undertaking wide choices. These arrangements can convey a multi-billion $ effect.

On the other hand, he has held different key positions in MNCs like Citigroup, HSBC, FICO and GE over various parts. However, he was in charge of setting up and overseeing examination bunches in the zones of protection investigation, buyer back and retail credit hazard administration and accumulated practical aptitude crosswise over advertising and hazard investigation. Especially, he is center around nonstop learning and expert improvement for self and the groups he has driven.

Education- A graduate from Indian Statistical Institute (ISI), Calcutta, Nitin has more than 17 long stretches of broad involvement in the field of prescient examination and information science ventures. He is additionally a functioning speaker and specialist at driving information science gatherings.

With this, you can also explore the below-mentioned names who not only worked as a Data Scientist but also have great respect in Data world.

Kirk Borne, Gilberto Titericz Jr., Sergey Yurgenson, Owen Zhang, Oliver Grisel

Conclusion

The best 10 names were finished up in light of different parameters like family, licenses, papers and specialized productions created, rivalries took an interest, spearheading work, information, and materialness of apparatuses, capacity to persuade various partners through information bits of knowledge and some more.

At this point, I am certain you would likewise have seen a typical characteristic among every one of these information researchers. Every one of these individuals is either determined by a dream or they’re settled in affection for information science. So if information science is your affection, I would recommend taking after these good examples intently and you’ll be on your way to significance!

Finally, I’ve recorded the best information researcher over the world. These individuals are experts in their own particular fields and energetically look for chances to spread information science learning the world over.