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

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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 Success Story of Big Data Tooling

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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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DexLab Analytics’ AUGUST OFFER: Everything You Need to Know Of

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DexLab Analytics’ AUGUST OFFER: Everything You Need to Know Of

We are happy to announce that we’re rolling some good news your way – DexLab Analytics is all set to launch exhaustive modules in Deep Learning with AI starting with Artificial Neural Networks using Python, MS Excel, Dashboards, VBA Macros, Tableau BI, Visualization and Python Spark for Big Data from September 1, 2018. The course modules are on in-demand skills and they are taking the world quite by a storm.

DexLab Analytics’ AUGUST OFFER

Big data, data science and artificial intelligence are buzz words these days. More and more people are coming forward and showing keen interest on these nuanced notions that solves real-world problems. This is why we didn’t want to fall behind. We understand the importance of data in this digitized world, and accordingly have chalked out our intensive industry-ready courses.

Deep Learning and AI starting with Artificial Neural Networks using Python course module is a 30-hour long training program that gives exposure to MLP, CNN, RNN, LSTM, Theano, TensorFlow and Keras. It includes more than 8 projects out of which a couple of focuses on development of models in to Image and Text recognition. MS Excel, Dashboards and VBA Macros certification is curated by the expert consultants after combining industry expertise with academician’s knowledge. The course duration is in total of 24 hours and is conducted by seasoned professionals with more than 8 years of industry experience specific to this budding field of science.

DexLab Analytics’ August Offer is On Machine Learning & AI

DexLab Analytics’ August Offer is On Machine Learning & AI

Next, we have30-hour hands-on classroom training on Tableau BI & Visualization certification, which teaches young minds how graphical representation of data unlocks company future trends and take quicker decisions. Tableau is one of the fastest evolving BI and data visualization tool. With that in mind, we offer a learning path to all you students by framing a structured approach coupled with easy learning methodology and course curriculum.  

DexLab Analytics Offers MS Excel, Dashboards and VBA Macros Certification!

DexLab Analytics Offers MS Excel, Dashboards and VBA Macros Certification!

Lastly, our Big Data with PySpark certification is another gem in the learner’s cap: the Spark Python API (PySpark) exposes users to the Spark Programming model with Python. Apache Spark is an open source and is touted as a significant big data framework for pivoting your tasks in a cluster. The main objective of this course is to teach budding programmers how to write python code using map-reduce programming model. The 40-hours hands-on classroom training will talk about Big Data, overview of Hadoop, Python, Apache Spark, Kafka, PySpark and Machine Learning.

Now, first 12 students who happen to register for each course on or before 30th August, 2018 will get alluring discount offer on the total course fee. Interesting, isn’t it? So, what are you waiting for? Go, grab all the details about AUGUST OFFER: to register, call us at +91 9315 725 902 / +91 124 450 2444 or hit the link below – www.dexlabanalytics.com/contact

 

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

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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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