Datamatics Global Services Limited

Datamatics is a leading provider of IT Services & Solutions, BPO and Technology Consulting. Helps in client's digital journey by leveraging technology.

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Build modern data platform with Apache Hadoop (data lakes)

Over the last few years, the data type and data quality have varied dynamically. Besides, the data volumes have increased exponentially. As a result, the traditional data warehouses, which are accustomed to process only structured data, are finding it increasingly difficult to store, process, and analyze at scale leading to bottle necks and task failures.  A data lake, built using Apache Hadoop, on-premise or on-cloud is fast becoming the newage solution for storing, processing, and analyzing multi-variate and high volume data in real-time.

Benefits of data lakes over data warehouses:

Data lakes integrate seamlessly with traditional database systems, analytics tools, and query engines used for business reporting. They can systematically extend a traditional data warehouse. At a broad level, data lakes provide the following benefits:

Distributed File system: It offers a highly scalable, fault-tolerant, distributed storage. It works concurrently with a number of data access applications through a YARN service.

YARN: It simultaneously allows multiple data processing engines provide analytics at scale; for example: interactive SQL, real-time streaming, data science, analytics workbench, and batch processing.

Support for Apache Spark: The augmented technology offers lightning fast unified analytics for large scale data processing. It allows writing parallel applications in Java, Scala, Python, R, and SQL.

Reduced TCO of data analysis: Data lakes reduce the cost of data management and analytics. Organizations experience reduced time-to-insights as well as deployment from days to minutes.

Unified storage platform: It reduces the number of data marts, employs business centric rules, and helps access policies within the same storage platform.

New insight creation: With a wide range of analytics tools, the platform helps quickly answer queries with reduction in time for insights and complex analytics.

Business Impact:

Data lakes offer a centralized repository, which is scalable across innumerable machines.

They allow ingesting, storing, processing, and transforming both structured and unstructured data, unlike data warehouses.

They empower a business with multi-functional tools such as data discovery, reporting, advanced analytics, and visual reporting on stored data irrespective of the native format.

They offer to draw insights in real-time and access it on-demand.

Best Practices:

Here are some best practices for building a data lake solution as a new initiative or as a re-architecture of a data warehouse:

Configure data lakes to be flexible and scalable for aggregating and storing all types of data.

Include Big Data Analytics components, which support data encryption, search, complex analysis, interactive analytics, and querying.

Implement access control policies and data security mechanisms to protect the stored data.

Provide data search mechanisms for quick and easy search and retrieval towards performing 360 degree analysis.

Ensure data movement for allowing import of any amount of data in its native format from disparate sources into a unified storage.

Securely store, index, and catalog data for allowing easy understanding and search of data streaming from mobile apps, IoT devices, and social media.

Perform comprehensive analytics using popular Big Data frameworks, such as Apache Hadoop and Spark without moving the data to a separate analytics system.

Use Machine Learning to derive valuable insights and perform self-learning using models, predict outcomes, and suggest actions for achieving optimal results.

Use BI tools, which seamlessly integrate with the data lake platform, to provide faster business analytics, dashboards and visualizations that are accessible from any browser and mobile device.

Strategies to extend a traditional data warehouse using data lake:

Retain the frequently used data in the warehouse and offload the unused data and ETL workload to data lake repository. Use a Big Data Analytics framework such as Apache Spark to perform fast in-memory analytics and maintain business continuity.

Migrate data in batches by using Network File System (NFS) or Apache Sqoop or real-time methods such as Kafka Connect. Subsequently, store the data in Hive tables or Parquet or Avro files.

Use unified SQL engines to deliver data to Business Intelligence teams. Leverage stored data in tables using BI tools. BI teams can query the offloaded data using SQL whereas the Data Science teams can analyze the newly sourced data using Analytics workbench.

Data lakes on cloud:

Data lakes augment data storage, intelligent processing, and complex analytics, especially on cloud platforms such as Infrastructure as a Service (IaaS). Data lakes usage can also be extended through Data Analysis (DAaaS) platform:

Data lake analytics: The goal is achieved by scaling the data storage and processing over the Infrastructure as a Service (IaaS) platform, which is provided by the likes of AWS and Microsoft Azure. With IaaS, organizations can develop and run massive parallel data transformations and processing programs without business overheads.

On-premise or on-cloud Apache Spark and Hadoop Services: AWS EMR and Azure HDInsights provide a fully managed cloud-based Hadoop cluster with Analytics capabilities and extended support for Machine Learning libraries. It helps to quickly spin-up the cluster on-demand and scale-up and down based on organization requirements and needs.

Data lake storage to power Big Data Analytics: AWS s3 powered data lake solutions provide massively scalable and secure storage. They are highly available, designed to deliver 99.999999999% durability, and store data for millions of applications. They also provide ‘query in place’ functionality, which allows running a query on the data set at rest. AWS s3 is supported by the large community of third-party applications and AWS services.

In Summary:

Data lakes solve challenges related to business intelligence and analytics. However, business needs are constantly evolving. Future-proofing data lake implementations, which evolve with the organization’s business needs, are the way ahead. Data lakes built on Hadoop platform empower the businesses to grow around existing and new data assets and easily derive business insights without limitations.

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6 top benefits of RPA on cloud

Robotic Process Automation (RPA) on cloud not only accelerates the amalgamation process but also improves business agility. As we all know, RPA is the fastest mode of integrating data across multiple systems. Cloud of course leverages this technology further. The data of the acquired entity can be on-cloud or on-premise from where RPA can bring it to a vantage point. Similarly, data of the acquiring entity can be on their premise. This convergence provides both the entities (one technologically advanced and the other legacy system based) a levelled turf and in a way a critical path or the least time for quick integration.

Some of the top benefits of RPA on cloud are:

Multi-tenant: A few bot licences can be used to automate many processes.

Pre-built optimized cloud service: The service provider can deploy bots in Build-Operate-Transfer (BOT) mode to quickly start operations.

Replicability and scalability: The converged technology provides unlimited flexibility to scale up operations and replicate the process modules.

Results seen in days: The effort is speeded up and completed in a few days. Massive scale integration usually takes many months.

Secure operations: Bots have read only access of scripts and cannot be reached from outside the cloud environment. Enterprise grade security controls secure the cloud infrastructure.

Anytime, anywhere monitoring: The deployed bots and the converged infrastructure can be monitored from around the globe in a 24x7 mode.

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Simplify product review analysis using Deep Learning and Natural Language Processing

A plethora of products are available in a highly consumerized world. As it is a ‘buyer’s market’, people prefer to read the product online reviews before purchasing them. Product review analysis per se also helps companies get consumer feedback and add value to their product. Though in both the cases, reading massive amounts of inputs provided by consumers in an unstructured format is an extremely lengthy and cumbersome process. Artificial Intelligence (AI) technologies such as Natural Language Processing (NLP) and Deep Learning help to analyze vast amounts of product review data and decipher consumer sentiment as positive, negative, or neutral.

A modular approach:

The solution to this business challenge comprises of four modules, which greatly simplify the review task:

Review classification: By using Word Embedding and Deep Learning, the module classifies the product review in to four categories – Query, Complaint, Praise, and Suggestion. Word Embedding uses Word2Vec and GloVe mechanism.

Feature capture: By using Advanced NLP and Topic Modeling, the module extracts the feature from the review.

Topic capture: By using Latent Dirichlet Allocation (LDA), the module extracts the topic from the review.

Sentiment analysis: By using Deep Learning methods such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM), the module captures the sentiment including sarcasm.

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Create a highly accurate business summary using Deep Learning algorithms

In complex business functions such as organizational audits, having quick data summaries at your disposal for analysis is a relief. It is explicitly so when piles of free text data are looming large and need to be analyzed for taking informed actions. Besides, using increasing quanta of manpower to expedite the task at hand seems unfeasible as it tremendously increases the running business cost. Text Summarization, powered by Deep Learning (Deep Neural Networks or DNN) and Natural Language Processing (NLP), is the answer to this business challenge.  DNN and NLP being subsets of Artificial Intelligence (AI) / Machine Learning (ML), help machine read, summarize, and analyze even unstructured data for better decision making.

What is Text Summarization?

Text Summarization is the process of automatically generating natural language summary from a lengthy input document while capturing the important points. The summarized text is appended as metadata to the digitized document. This process helps in easy and fast search as well as retrieval of the document and its related information at a later date. Essentially, Text Summarization is a technology framework, which is built to capture and organize a variety of unstructured data from different sources.

The goal of Text Summarization is to retain maximum information along with optimum shortening of text ‘without altering the meaning of the text’. Most summarization systems utilize ‘extractive approaches’. However, an abstractive approach is an upcoming concept, which provides a realistic summary.

Different types of approaches – Extractive Summarization & Abstractive Summarization:

In Extractive Summarization, systems form summaries by copying parts of the source text through some measure of weightage and importance given to words and then combining those parts of sentences together to render a summary. Here, the importance of the sentence is based on linguistic and statistical features.

In contrast, Abstractive Summarization attempts to produce a ‘bottom-up summary’. In Abstractive Summarization, systems generate new phrases, possibly rephrasing or using words that were not in the original text. Naturally, abstractive approaches are harder. For creating a perfect abstractive summary, the model has to first truly understand the document and then try to express that understanding in a condensed format, using new words and phrases.

Superiority of Abstractive over Extractive:

Extractive Text Summarization is a very old technique and it’s not proper summarization. When humans summarize a piece of text, they read it entirely to develop an understanding of the content and then write a summary highlighting the main points. However, Extractive Summarization gives importance to word weightages only.

Abstractive Summarization is a process where the algorithms are capable to build own sentence just like a human. Deep Learning sequence models have shown promising results in Abstractive Text Summarization as compared to Extractive Summarization.

How does DNN work?

Traditional rule-based AI is not able to generate new sentences. The recent advancements in DNNs have changed the scenario. DNNs use ‘sequence-to-sequence’ model to predict a new sentence. Long Short Term Memory (LSTM) is one such DNN model used in Abstract Summarization.

An LSTM network is a recurrent neural network that has LSTM cell blocks in place of standard neural network layers. It feeds back the output of a neural network layer at ‘time t’ to the input of the same network layer at ‘time t + 1’. Recurrent neural networks are “unrolled” programmatically during training and prediction. Here at each time step, a new word is supplied – that is, the output of a previous cell is supplied to the network at each time step. A new word/sequence value is progressively concatenated to the previous output.

After processing, the framework can predict new words, sentences, or phrases. In this manner, the ‘LSTM concept’ can be used to build the Abstractive Text Summarization framework.

The framework uses encoder-decoder architecture model for summarization. Both encoder and the decoder sub-models are trained jointly, meaning at the same time. The Text Summarization framework uses an encoder for reading the source document and encoding it to an internal representation. The decoder is a language model responsible for generating each word in the output summary using the encoded representation of the source document.

In summary:

Text Summarization framework is a very powerful Deep Learning powered framework. It is based on Advanced NLP and DNNs. This framework is capable of generating new summarized logical sentences or group of words from a lengthy document. It can be used in many business use cases such as document summarization, email summarization, customer review summarization, etc. So rest assured. By using this framework powered by NLP and DNN, your next audit assignment will be a breeze.

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12 interesting Facts about Big Data.  Know more here:http://goo.gl/qmNOnA

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