Data. Be it Science, Business, Economics or Finance, it’s all about Data. In a time when data equals valuable information that can make or break, its sorting, storing and integration has become one of the most important practices. Informatica tools for Data Integration aim to do just that. Its components, focused on Data Integration and ETL processes, form the basis for establishing and maintaining enterprise wide data warehouses.
Data Warehouses are central repositories which store data and information from multiple and diverse sources. It is a system used for reporting and data analysis which incorporates data stores and conceptual, logical, and physical models to support business goals and end-user information needs. A data warehouse lays the foundation for a successful BI program.
The ETL product of Informatica is the Informatica Power Center which consists of the client tools, repository and server. The PowerCenter server and repository server make up the ETL layer, which completes the ETL processing. Informatica PowerCenter is a widely used extraction, transformation and loading (ETL) tool used in building enterprise data warehouses. The components within Informatica PowerCenter aid in extracting data from its source, transforming it as per business requirements and loading it into a target data warehouse. The PowerCenter server executes tasks based on work flow created by work flow managers. The work flows can be monitored using a work flow monitor. Jobs inside the program are designed in a mapping designer, which creates mapping between source and target. Mapping is a pictorial representation about flow of data from source to target. Transformations such as aggregation, filtering and joining are major examples of transformation.
One of major reason for Informatica ETL tool’s success is its capability of enabling Lean Integration. Lean Manufacturing is a common concept in manufacturing industry to avoid waste. Informatica leverages the same integration model. Informatica comes with internal scheduler unlike many other ETL tools where one needs to use third party schedulers, an added advantage for those who are not using enterpriser scheduler. For many organizations, taking a proprietary scheduler is not strategic, especially if module is in POC phase for business. Informatica follows mainstream marketing strategy where it leverages paper work, press release, web forums and network community. This provides it leading edge in ETL space.
Informatica is the best ETL tool availabale in the market and is used by many established companies such as ADP, Allianz, American Airlines, CA Technologies, CERN, SIEMENS, CSC and Qualcomm among others. Head on to our Informatica Live Online Course and learn more about this amazing tool.
Node.js is intended to run on a dedicated HTTP server and to employ a single thread with one process at a time. The applications are events-based and run asynchronously. Code built on the Node platform does not follow the traditional model of receive, process, send, wait, receive but instead the incoming requests are processed in a constant event stack, sending small requests continuously without waiting for responses. This is a shift away from mainstream models that run larger, more complex processes and run several threads concurrently, with each thread waiting for its appropriate response before moving on.
You can learn more about Node.js and how to develop web applications using it with our Node.js online tutorial that will take you through the length and breadth of all the core and associated concepts. Happy learning!
The charm of an MBA or Masters in Management(MiM) degree has always been appealing, and more so getting it from a world class B-School. Add to this the prestige that comes with a tag of being a Management grad with a fancy job and title, and you have hordes of people applying to numerous top colleges every year! The first step to an MBA or MiM application is the feared GMAT exam. But is it really that scary and demanding to crack? Let’s finds out.
The Graduate Management Admission Test (GMAT) is a computerized aptitude test that aims to gauge the analytical, writing, quantitative, verbal, and English reading skills as a parameter for admission into Management and MBA programs in universities worldwide. The exam itself is governed by the Graduate Management Admission Council (GMAC), and the GMAT score is accepted across the globe in over 2,100 institutions and 5,900 programs.
The exam is quite extensive and intensive, with four specific test sections each testing a particular skill. The Analytical Writing Assessment (AWA) is a single 30 minute section with a writing task, where a test taker analyses an argument and then presents a reasoning and critique of the argument. This is graded on a scale of 6 points, with 6 indicating an outstanding essay. A fairly recent addition of the exam has been the Integrated Reasoning (IR) section (in 2012), designed to measure a test taker’s ability to evaluate data presented in multiple formats from multiple sources. This consists of twelve questions in four formats with a score ranging from 1 to 8. The questions asked in this section were identified in a survey of 740 management faculty worldwide as important for today’s incoming students. Scores of both these sections are not counted against the GMAT score, but these scores are considered holistically when considering an application.
The Quantitative and Verbal sections are the ones whose score contribute to the final GMAT score. The Quant sections seeks to measure the ability to reason quantitatively, solve quantitative problems, interpret graphic data, and analyze and use information given in a problem. There are two sub sections here: problem solving and data sufficiency. The entire section is graded from 0-60, with scores being reported for scores between 6 and 51, and is considered a tough part of the exam. The other scoring section is the Verbal, which seeks to measure the test taker’s ability to read and comprehend written material, reason and evaluate arguments and correct written material to express ideas effectively in standard written English. It contains reading comprehension, critical reasoning, and sentence correction questions with the entire sections graded from 0-51.
Phew! That’s quite a handful! It is not scary but is a revered test, with most people still picking to take the GMAT though the GRE is accepted for Management programs’ admissions these days. There is a certain exclusivity and elite characteristic associated with the GMAT making it the most popular choice. Planning to take the GMAT? Want to ace it? Look no further than our GMAT Test Prep which will help you get the score you deserve. Log on for some amazing learning!
Big Data has been on the tongue of every Data & Analytics expert in the past couple of years. The use and processing of Big Data for business decisions has taken the world by storm. Right from consumer marketing to political campaigns to finance and science, everyone is lapping up on the immense power of Big Data & Predictive Analytics. Taking advantage of this surge, many players have come into the market with their platforms that can quantify Big Data and produce valuable information through it. The most well-known ones include Apache Hadoop, Apache Spark, SAP HANA, Google BigQuery, and Oracle Big Data Appliance. Let’s take a sneak peak at what each of these is.
Apache Hadoop is by far the most popular and widely used Big Data platform. It is a distribution data process framework of the Map-Reduce method and is equipped to handle large-datasets on computer clusters easily. Hadoop splits files into large blocks and distributes them amongst the nodes in the cluster. It takes advantage of the data locality for quick and efficient data processing and works better than a supercomputer architecture relying on a parallel file system in this regard.
SAP HANA is a memory-based storage made from SAP. Its characteristic is to organize a system optimized to analysis tasks, such as OLAP. If all data is inside system memory, maximizing CPU utilization is crucial and the key point is to reduce bottlenecks between memory and CPU cache. In order to minimize Cache miss, consecutive data for processing within the given time is more advantageous; meaning that configuration of column-oriented tables could be favorable when analyzing many OLAP.
Apache Spark is the new kid on the block, offering lightning fast Big Data computing. Seemingly, Spark’s multi-stage in-memory primitives provide performance up to 100 times faster for certain applications. It is well suited for machine learning algorithms owing to its feature of allowing data to be loaded into a cluster’s memory & repeated querying. It runs on top of existing Hadoop cluster and can access Hadoop data store (HDFS), can also process structured data in Hive and Streaming data from HDFS, Flume etc.
Google BigQuery is a web service from the big daddy of data. It is an IaaS (infrastructure as a Service) working in conjunction with Google Storage to interactively analyze extremely huge datasets. It can query massive datasets fast, without being too heavy on the pocket. It enables super-fast, SQL queries against append-only tables, using the processing power of Google’s infrastructure.
Oracle Big Data Appliance is a high-performance, secure platform for running diverse workloads on Hadoop and NoSQL systems. With Oracle Big Data SQL, Oracle Big Data Appliance extends Oracle’s industry-leading implementation of SQL to Hadoop and NoSQL systems. It is a new architecture for SQL on Hadoop, seamlessly integrating data in Hadoop and NoSQL with data in Oracle Database. It radically simplifies integrating and operating in the big data domain through two powerful features: newly expanded External Tables and Smart Scan functionality on Hadoop.
Intrigued and fascinated yet? The adoption and evolution of Big Data processing & analytics is growing at a massive rate. This would be the absolute perfect time for Data enthusiasts & professionals to get their hands dirty and equip themselves with the proficiency in a Big Data platform. Come checkout our lot of the Big Data courses and we assure you this will be the best learning you will ever receive. Happy learning!
Data Science & Analysis is a cutting edge domain of technologies and theories, the concepts of which are applicable in almost every turf. We deal with huge amounts of data every minute of every day. Analyzing Data efficiently helps us make head or tail of this data and aids in presenting it in a manner that is understandable, leading to sound business decisions. Data Analysis finds use in every domain, from ecommerce to finance to government, and everything in between. Data Analysis is the queen bee and Data Analysts are the prized possessions every company is after.
In this age of information, we are increasingly dependent on data analysis to develop a visualization of the data at hand. Data Analysis is a decidedly inter-disciplinary field employing the theories and practices of mathematics, statistics and information technology. It is essentially the compilation and interpretation of information from large data sources. Requiring a multi-domain skill set, it is currently one of the fastest growing and in-demand analysis sciences.
Data Analysis, its interpretation & representation in R form the basis for Predictive Analytics which finds use in a wide ambit of applications. Its techniques help analyze a range of available data to make predictions about future or undetermined events. The uses vary from companies trying to gauge customer reaction to a product to healthcare professionals aiming to determine high risk patients. This huge operational field of practice has made data analysis, predictive analytics and its tools much revered.
R is a popular programming language and software environment for statistical computing and graphics. R arguably reduces complexity for such analysts because it incorporates the macro and matrix languages, among other things. It is capable of putting big numbers into real-world use with leading applications in business analytics, consumer behavior, trend prediction, social sciences. The theories of Data Science and Data Analysis are applied in areas such as cloud computing, information visualization, information security, database integration, machine learning.
Want to learn more about Data Analysis and how to use the R programming tool for data representation and visualization? Head on to our Data Analytics using R course and learn everything you want to know! Happy learning!