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It’s Cracked: Now Increase Your Salary as an IT Professional

It’s Cracked: Now Increase Your Salary as an IT Professional

Keen to increase your salary – perhaps you’ve accomplished a difficult task and in a position to ask for a salary-hike? Or maybe, it’s time you want to make a switch?

Whatever be the reason, in both the abovementioned cases, the crux is a salary hike – but how to do it well? Salary negotiations are one of the toughest battle fought inside the boardrooms. Interestingly, only 39 percent of professionals even tried to negotiate a higher salary during their last job offer, says a 2018 survey of close to 3,000 people conducted by global staffing firm Robert Half.

Below, we’ve handpicked few of the best ways to enhance your salary without raising an eyebrow – scroll below for such key pieces of advice:

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Never Lose Your Calm

Emotional intelligence is to be demonstrated. Not impatience. You are yet to get that job, and your salary negotiation skill is a reflection how you are going to do business, while remaining calm under stressful situations.

Do Your Homework

“Be confident in your own skin! Your salary negotiations can deeply suffer owing to a lack of preparation,” says Jim Johnson, senior vice president at Robert Half Technology. This firm generates an annual salary guide for more than 75 positions in IT field, with data!

In addition, Mr. Johnson supports weighing the competitiveness of your current pay. That’s important. Not only subject to your role or designation, but also to your respective skills, vertical industry and area – including security and data analytics.

Certifications Help

Today, an array of certified and non-certified in-demand skills is available in the market. As a result, IT professionals are found shelling extra pounds for these certifications – an average of 7.6 percent of base salary for a single certification and 9.4 percent of base salary on average for certain single, non-certified skills.

Amidst all, Apache Spark Progamming Training, Data Science, Cryptography and Penetration Testing are the hottest in line. Python Course in Delhi NCR, Artificial Intelligence and Risk Analytics are next to follow.

Other than that, open source skills are quite popular – especially those that concerns DevOps, cloud and containers.

Imbibe Soft Skills As Much As You Can

Developing soft skills is an art! And in this tough age of digital transformation, IT professionals have to constantly to work in cross-functional teams with fellows from different arenas of the business, as well as clients and partners who have zero tech skills.

For this and more, you have to have a good command over English, undying patience and understand people, what they have to say! No wonder, many IT bigwigs say these soft skills are not as soft as they sound – sometimes, it’s really hard to explain and teach people from different parts of the industry.

“It’s funny that we even talk about these skills as ‘soft,’ because they are very hard to master and are frequently the cause of more trouble than lack of ‘hard’ skills,” shares Anders Wallgren, CTO at Electric Cloud.

Care to nurture your data analytics skill? The expert guys at DexLab Analytics are here!

 

The blog has first appeared on ― enterprisersproject.com/article/2018/11/what-best-way-increase-your-salary-it-professional

 

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The ABC Basics of Apache Spark

The ABC Basics of Apache Spark

Amazon, Yahoo and eBay has embraced Apache Spark. It’s a technology worth taking a note of. A bulk of organizations prefers running Spark on clusters along with thousands of nodes. Till date, the biggest known cluster consists of more than 8000 nodes.

Introducing Apache Spark

Spark is basically an Apache project tagged as ‘lightning fast cluster computing’. It features a robust open-source community and is the most popular Apache project right now.

Spark is equipped with a faster and better data processing platform. It runs programs faster in memory as well as on disk as compared to Hadoop. Furthermore, Spark lets users write code as quickly as possible – after all, you’ve more than 80 high-level operators for coding!

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Key elements of Spark are:

  • It offers APIs in Java, Scala and Python in support with other languages
  • Seamlessly integrates with Hadoop ecosystem and other data sources
  • It runs on clusters controlled by Apache Mesos and Hadoop YARN

Spark core

Ideal for wide-scale parallel and distributed data processing, Spark Core is responsible for:

  • Communicating with storage systems
  • Memory management and fault recovery
  • Arranging, assigning and monitoring jobs present in a cluster

The nuanced concept of RDD (Resilient Distributed Dataset) was first initiated by Spark. An RDD is an unyielding, fault-tolerant versatile collection of objects that are easily operational in parallel. It can include any kind of object, and supports mainly two kinds of operations:

  • Transformations
  • Actions

Spark SQL

A major Spark component, SparkSQL queries data either through SQL or through Hive Query Language. It first came into operations as an Apache Hive port to run on top of Spark, replacing MapReduce, but now it’s being integrated with Spark Stack. Along with providing support to numerous data sources, it also fabricates several SQL queries with code transformations, which makes it a very strong and widely-recognized tool.

Spark Streaming:

Ideal for real time processing of streaming data – Spark Streaming receives input data streams, which is then divided into batches only to be processed by Spark engine to unleash final stream of results, all in batches.

Look at the picture below:

The Spark Streaming API resembles Spark Core – as a result, it becomes easier for programmers to tackle for batch and streaming data, effortlessly.

MLib

MLib is a versatile machine learning library that comprises of numerous fetching algorithms that are designed to scale out on a cluster for regression, classification, clustering, collaborative filtering and more. In fact, some of these algorithms specialize in streaming data, such as linear regression using ordinary least squares or k-means clustering.

GraphX

An exhaustive library for fudging graphs and performing graph-parallel operations, GraphX is the most potent tool for ETL and other graphic computations.

Want to learn more on Apache Spark? Spark Training Course in Gurgaon fits the bill. No wonder, Spark simplifies the intensive job of processing high levels of real-time or archived data effortlessly integrating associated advanced capabilities, such as machine learning – hence Apache Spark Certification Training can help you process data faster and efficiently.

 
The blog has been sourced fromwww.toptal.com/spark/introduction-to-apache-spark
 

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The Success Story of Big Data Tooling

The Success Story of Big Data Tooling

The world of hadoop data tooling is flourishing. It’s being said, Hadoop is shifting from possible data warehousing to an accomplished big data analytics set-up.

Back in the day, right after Hadoop at Yahoo was first invented, proponents of big data asserted its potential for substituting enterprise data warehouses, framed on business intelligence.

Open source Hadoop data tooling became a preferred choice more as an alternative to those insanely expensive existing systems – as a result, over time, the focus shifted to expanding existing data warehouses and more. Intricate Hadoop applications today are known as data lakes and of late big data tooling is found swelling beyond meager data warehouses.

“We are seeing increasing capabilities on the Hadoop and open source side to take over more and more of the corporation’s data and workloads, including BI,” said Mike Matchett, an analyst and founder of the Small World Big Data consultancy.

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Self Service and Big Data

In August, Cloudera launched Workload XM management services designed exclusively for cloud-based analytics. Alternatively, the company built a hybrid Cloudera Data Warehouse and a Cloudera Altus Data Warehouse, capable of running over both Microsoft Azure clouds and AWS.

The main objective of management services is to bring forth some visibility into various data workloads. Workload XM is constructed to aid administrators in presenting reliable service-level agreements for self-service analytics applications – says Anupam Singh, GM of Analytics at Cloudera, Palo Alto, Calif.

Importantly, Singh also mentioned that the cloud warehouse offers encryption for data both at still and in motion, and provides a better view into the trajectory of data sets in analytics workloads. Such potentials have gained momentum and recognition as well as GDPR and other programs.

However, all these discussions boil down to one point, which is how to increase the use of big data analytics. “Customers don’t look at buzzwords like Hadoop and cloud. But they do want more business units to access the data,” he added.

Data on the Wheels

Hadoop player, Hortonworks is a Cloud aficionado. In June, the company broadened its Google Cloud existence with Google Cloud Storage support. Enhancing real-time data analytics and management is a priority.

Meanwhile, in August, Hortonworks churned out Streams Messaging Manager (SMM) with an objective of handling data streaming and provide administrators comprehensive views into Kafka messaging clusters. They have increasingly become popular amongst big data pipelines.

These management tools are crucial for moving Hadoop-inspired big data analytics into production capacities, where in data warehouses fails performing – thus, recommendation engines and fraud detection appears to be a saving grace!

Meanwhile, Kafka-related capabilities in SMM are going on getting advanced and with recently released Hortonworks DataFlow 3.2, the performance for data streaming amplified.

R Adaptability

Similar to its competitors, MapR has bolstered its capabilities beyond its original scope of being used as a mere data warehouse replacement. Early this year, the organizers released a new version of its MapR Data Platform equipped with better streaming data analytics and new item data services that would easily work on cloud as well as premises.

As final thoughts, the horizon of Hadoop is expanding, while data tooling keeps modifying. However, today, unlike before, Hadoop is not only the sole choice for doing data analytics – the choice includes Apache Spark and Machine Learning. All being extremely superior and effective when put to use.

If you are looking for Apache Spark Certification, drop by DexLab Analytics. Their Apache Spark Training program is extremely well-crafted and in sync with industry demands. For more, visit the site.

 

The article has been sourced from — searchdatamanagement.techtarget.com/news/252448331/Big-data-tooling-rolls-with-the-changing-seas-of-analytics

 

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A Comprehensive Article on Apache Spark: the Leading Big Data Analytics Platform

A Comprehensive Article on Apache Spark: the Leading Big Data Analytics Platform

Speedy, flexible and user-friendly, Apache Spark is one of the main distributed processing frameworks for big data in the world. This technology was developed by a team of researchers at U.C. Berkeley in 2009, with the aim to speed up processing in Hadoop systems. Spark provides bindings to programming languages, like Java, Scala, Python and R and is a leading platform that supports SQL, machine learning, stream and graph processing. It is extensively used by tech giants, like Apple, Microsoft and IBM, telecommunications industry and games organizations.

Databricks, a firm where the founding members of Apache Spark are now working, provides Databricks Unified Analytics Platform. It is a service that includes Apache Spark clusters, streaming and web-based notebook development. To operate in a standalone cluster mode, one needs Apache Spark framework and JVM on each machine in a cluster. To reap the advantages of a resource management system, running on Hadoop YARN is the general choice. Amazon EMR and Google Cloud Dataproc are fully-managed cloud services for running Apache Spark.

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Working of Apache Spark:

Apache Spark has the power to process data from a variety of data storehouses, such as Hadoop Distributed File System (HDFS) and NoSQL databases. It is a platform that enhances the functioning of big data analytics applications through in-memory processing. It is also equipped to carry out regular disk-based processing in case of large data sets that are unable to fit into system memory.

Spark Core:

Apache Spark API (Application Programming Interface) is more developer-friendly compared to MapReduce, which is the software framework used by earlier versions of Hadoop. Apache Spark API hides all the complicated processing steps from developers, like reducing 50 lines of MapReduce code for counting words in a file to only a few lines of code in Apache Spark. Bindings to well-liked programming languages, like R and Java, make Apache Spark accessible to a wide range of users, including application developers and data analysts.

Spark RDD:

Resilient Distributed Dataset is a programming concept that encompasses an immutable collection of objects for distribution across a computing cluster. For fast processing, RDD operations are split across a computing cluster and executed in a parallel process. A driver core process divides a Spark application into jobs and distributes the work among different executor processes. The Spark Core API is constructed based on RDD concept, which supports functions like merging, filtering and aggregating data sets. RDDs can be developed from SQL databases, NoSQL stores and text files.

Apart from Spark Core engine, Apache Spark API includes libraries that are applied in data analytics. These libraries are:

  • Spark SQL:

Spark SQL is the most commonly used interface for developing applications. The data frame approach in Spark SQL, similar to R and Python, is used for processing structured and semi-structured data; while SQL2003-complaint interface is for querying data. It supports reading from and writing to other data stores, like JSON, HDFS, Apache Hive, etc. Spark’s query optimizer, Catalyst, inspects data and queries and then produces a query plan that performs calculations across the cluster.

  • Spark MLlib:

Apache Spark has libraries that can be utilized for applying machine learning techniques and statistical operation to data. Spark MLlib allows easy feature extractions, selections and conversions on structured datasets; it includes distributed applications of clustering and classification algorithms, such as k-means clustering and random forests.

  • Spark GraphX:

This is a distributed graph processing framework that is based on RRDs; RRD being immutable makes GraphX inappropriate for graphs that need to be updated, although it supports graph operations on data frames. It offers two types of APIs, Pregel abstraction and a MapReduce style API, which help execute parallel algorithms.

  • Spark Streaming:

Spark streaming was added to Apache Spark to help real-time processing and perform streaming analytics. It breaks down streams of data into mini-batches and performs RDD transformations on them. This design facilitates the set of codes written for batch analytics to be used in stream analytics.

Future of Apache Spark:

The pipeline structure of MLlib allows constructing classifiers with a few lines of code and applying Tensorflow graphs and Keras models on data. The Apache Spark team is working to improve streaming performance and facilitate deep learning pipelines.

For knowledge on how to create data pipelines and cutting edge machine learning models, join Apache Spark programming training in Gurgaon at Dexlab Analytics. Our experienced consultants ensure that you receive the best apache spark certification training.

 

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Foster your Machine Learning Efforts with these 5 Best Open Source Frameworks

Foster your Machine Learning Efforts with these 5 Best Open Source Frameworks

Machine Learning is rapidly becoming the mainstream and changing the way we carry out tasks. While many factors have contributed to this current boom in machine learning, the most important reason is the wide availability of open source frameworks.

’Open source’ refers to a program that is created as a collaborative effort in which programmers improve the code and share the changes within the community. Open source sprouted in the technological community in response to proprietary software owned by corporations. The rationale for this movement is that programmers not concerned with proprietary ownership or financial gain will produce a more useful product for everyone to use. 

Framework: It refers to a cluster of programs, libraries and languages that have been manufactured for use in application development. The key difference between a library and a framework is ‘’inversion of control’’. When a method is summoned from a library, the user is in control. With a framework the control is inverted- the framework calls the user.

If you are plunging full-fledged into machine learning, then you clearly need relevant resources for guidance. Here are the top 5 frameworks to get you started.

  1. TensorFlow:

TensorFlow was developed by the Google Brain Team for handling perceptual and language comprehending tasks. It is capable of conducting research on machine learning and deep neural networks. It uses a Python-based interface. It’s used in a variety of Google products like handling speech recognition, Gmail, photos and search.

A nifty feature about this framework is that it can perform complex mathematical computations and observe data flow graphs. TensorFlow grants users the flexibility to write their own libraries as well. It is also portable. It is able to run in the cloud and on mobile computing platforms as well as with CPUs and GPUs.

  1. Amazon Machine Learning (AML):

AML comes with a plethora of tools and wizards to help create machine learning models without having to delve into the intricacies of machine learning. Thus it is a great choice for developers. AML users can generate predictions and utilize data services from the data warehouse platform, Amazon Redshift. AML provides visualization tools and wizards that guide developers. Once the machine learning models are ready  AML makes it easy to obtain predictions using simple APIs.

  1. Shogun:

 Abundant in state-of-the-art algorithms, Shogun makes for a very handy tool. It is written in C++ and provides data structures for machine learning problems. It can run on Windows, Linux and MacOS. Shogun also proves very helpful as it supports uniting with other machine learning libraries like SVMLight, LibSVM, libqp, SLEP, LibLinear, VowpalWabbit and Tapkee to name a few.

  1. NET:

Accord.NET is a machine learning framework which possesses multiple libraries to deal with everything from pattern recognition, image and signal processing to linear algebra, statistical data processing and much more. What makes Accord so valuable is its ability to offer multiple things which includes 40 different statistical distributions, more than 30 hypothesis tests, and more than 38 kernel functions.

  1. Apache Signa, ApacheSpark MLibApache, and Apache Mahout:

These three frameworks have plenty to offer. Apache Signa is widely used in natural language processing and image recognition. It is also adept in running a varied collection of hardware.

Mahout provides Java libraries for a wide range of mathematical operations. Spark MLlib was built with the aim of making machine learning easy. It unites numerous learning algorithms and utilities, including classification, clustering, dimensionality reduction and many more.

 With the advent of open source frameworks, companies can work with developers for improved ideas and superior products. Open source presents the opportunity to accelerate the process of software development and meet the demands of the marketplace.

Boost your machine learning endeavors by enrolling for the Apache Spark training course at DexLab Analytics where experienced professionals ensure that you become proficient in the field of machine learning.

 

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How Data Analytics Is Shaping and Developing Improved Storage Solutions

Technology has penetrated deep into our lives – the last 5 decades of IT sector have been characterized by intense development in electronic storing solutions for recordkeeping.

 
How Data Analytics Is Shaping and Developing Improved Storage Solutions
 

Today, every file, every document is stored and archived safely and efficiently – rows of data are tabled in spreadsheets and stored in SQL relational databases for smooth access anytime by anyone, of course the authorized persons. Data is omnipresent. It is being found in data warehouses, data lakes, data mines and in pools. It is so much large in volume nowadays, that it can even be calculated in something like a Brontobyte.

 

Information is power. Data stored in archives are used to make accurate forecasts. And the data evaluation has begun within a subset of mathematics powered by a discipline named probability and statistical analysis.

 

Slowly, this discipline evolved into Business Intelligence that further into Data Science. The latter is the most sought after and well-paid career option for today’s tech-inspired generation. Grab a data science certification in Gurgaon and push your career to success.

 

Big Data Storage Challenges and Solutions

The responsibility of storage, ensuring security and provide accessibility for data is huge. Managing volumes and volumes of data is posing a challenge in itself – for example, even powering and cooling enough HDD RAID arrays to keep an Exabyte of raw data tends to break the bank for many companies.

 

Software-defined storage and flash devices are being deployed for big data storage. They promise of better direct business benefit. Also, increasingly Apache Spark Hadoop or simply Spark is taking care of the software side of big data analytics. Whether your big data cluster is developed on these open-source architectures or some other big data frameworks, it will for sure impact your storage decisions.

 

Hadoop is in this business of storage for big data for quite some time now. It is a robust open-source framework opted for suave processing of big data. It led to the emergence of server clusters and Facebook is known to have the largest Hadoop cluster containing millions of nodes.

 
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Now, the question remains where and how you proceed with Hadoop – there are so many differing opinions about how you approach Hadoop clusters, at times it may leave you exasperated. For that, we can help you here.

 

With a huge array of data at play, we suggest to deploy a dedicated processing, storage and networking system in different racks to avoid latency or performance issues. It is for the same reasons, we ask you to stay away running Hadoop in a virtual environment.

Instead, implement HDFS (Hadoop Distributed File System) – it is perfect for distributed storage and processing with the help of commodity hardware. The structure is simple, tolerant, expandable and scalable.

 

Besides, the cost of data storage should also be given a look at – cost should be kept low and data compression features should likely to be implemented.

For Big Data Hadoop certification in Delhi NCR, drop by DexLab Analytics.

 
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The Takeaway

Times are changing, and so are we. Big data analytics are becoming more real-time, hence better you scale up to real-time analytics. Today, data analytics have gone way beyond the conventional desktop considerations – it has now become a lot more, and to keep pace with the analytics evolution, you need to have sound storage infrastructure, where possible upgrades to computing, storage and networking is easily available and implementable.

 

To answer about big data or Hadoop, power yourself up with a good certification in Big Data Hadoop from DexLab Anlaytics – such intensive big data courses do help!

 

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