Data management platform tools

To Know more about Clinical Trial Data Management & SAS Clinical Programming and Data Conversion CDISC SDTM Standards! Placement assistance is provided to all the program participants. The Institute is in touch with several reputed Clinical Trial and Pharma companies where passing out students will be placed. The CDS institute has also tied up with several reputed Recruitment Process Outsourcing Contact: Courses Offered 1) Clinical Research 2) Clinical Data Management(EDC Tools) 3) SAS Clinical Programming 4) CDISC(SDTM.ADaM) Phone:

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Start your journey into data analytics with ExcelR's Data Analysis Course in Chennai. In six months, you'll go through an in-depth training program that prepares you for a successful career as a Data Analyst. The curriculum, developed by industry leaders, focuses on key skills such as database management, data analysis, and visualization techniques. Learn from instructors who have a decade of experience in the field. Master essential tools like MySQL, Python, and R, and apply your knowledge thro...

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Big Data & Hadoop Training in one month in Aptech Hazra Learn basics of Big-Data& Hadoop Smart Professional - Big Data Aptech's Smart Professional: Big Data course trains you in advance database technologies like Hadoop, used by more than half of Fortune 50 companies. The curriculum trains you from the basic to the advanced level, right from installing & configuring Hadoop, and maintaining the system, to using additional products that integrate with other systems. Course Details •Onlinevarsity - A learning app, exclusively for our students that provides access to study material, e-books, reference material, video tutorials, chats with experts & more •Move applications stored on a computer to a remote location & make them accessible online through standard browsers •Understand concepts & technologies of big data & its management •Develop dynamic web applications in PHP & use MySQL efficiently for those applications •Implement data security during data manipulation & access •Use NoSQL for storage & retrieval of data •DUse MongoDB and Apache Cassandra to handle large amounts of data across servers •Use Hadoop for distributed storage & processing of big data •Use PigLatin, Hive, Hbase for querying & managing databases •Perform analysis & reporting with big data tools Course Covers •Introduction to cloud computing •Introduction to big data •Data security •Working with NoSQL •Data management using MongoDB & Apache Cassandra •Fundamentals of Hadoop •Reporting & analytics with bigdata •Project COURSE DURATION Smart Professional - Big Data is an 8-month course. Classes are typically held 2 hours - a - day / 3 - days - a - week. ELIGIBILITY IT graduates / professionals / engineers

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Big data & Hadoop skills are in high demand in the industry Train to become a Hadoop developer & analystLearn various big data management and analysis tools like Hadoop, HDFS, MapReduce, Hive, Pig, NoSQL, Flume, Sqoop etc. Practice the concepts hands-on and learn the skills in a practical manner. Enrol now to avail an attractive discount. Call

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DATA STAGE TECHNOLOGY: What is Data stage?? Data Stage integrates data across multiple and high volumes of data sources and target applications. It integrates data on demand with a high performance parallel framework, extended metadata management, and enterprise connectivity. DataStage also has a set of Windows-based graphical tools that allow ETL processes to be designed, the metadata associated with them managed, and the ETL processes monitored. There are also There are also two DataStage engines, the “server engine” and the “parallel engine” Usage of DataStage within organization it has window Clients which connettto tthe Server on tthe Unix / Windows or Mainfframe platform.. The Clients can be used tto devellop,, deploy and run datastage jjobs.. In a deployment environment,, tthe jjobs can be kicked off tthrough scripts directly on Unixservers TYPES OF DATA STAGE CLIENT:: DATA STAGE ADMINISTRATOR DATASTAGE DESIGNER DATASTAGE MANAGER DATASTAGE DIRECTOR DATA STAGE TECHNOLOGY PARCTICE-:: INFRASTRUCTURE SETUP/MAINTENANCE SUPPORT SALES/ MARKETING KNOWLEDGE BASED REPOSITORY PARTNER MANAGEMENT TECHNICAL SUPPORT TRAINING AND TERM EVALUTION ADVANTAGES OF DATASTAGE- Helps for better business decisions; It is able to integrate data coming from all parts of the company; It helps to understand the new and already existing clients; We can collect data of different clients with him, and compare them; It makes the research of new business possibilities possible; We can analyze trends of the data read by him. CONCLUSION- DataStage has proved to be an excellent ETL tool within the industry. The need for Data Integration and Consolidation within organizations is fuelling the need for datastage. Data Transfer format landscape is gradually moving towards XML in every industry. PR3 Systems provides detailed training and consulting services for DataStage, Best Practices in DataStage Project Design and Helping organizations to consolidate their Data and Information Network To join Online Training session kindly feel free to contact with us Name - Madhurima Bose Contact No. - +91 - /

If you are looking for the Best platform to learn Data science, is an interdisciplinary field that uses statistical and computational methods to extract insights and knowledge from data. Its goal is to provide actionable insights, predictions, and recommendations based on data by using techniques such as machine learning algorithms, data visualization, and statistical analysis. Data science is used in various industries, including finance, healthcare, marketing, and technology, to solve problems such as fraud detection, risk management, and predictive maintenance. Data science provides numerous benefits to organizations, including better decision-making by identifying patterns, trends, and opportunities from data. It also improves efficiency by automating tasks and streamlining processes. Data science enables personalized customer interactions, leading to enhanced customer experience and loyalty. Organizations can reduce costs and gain a competitive advantage by optimizing their operations and providing better customer experience. Overall, data science is a valuable tool for organizations to improve their performance and achieve their business goals. Best platform to learn Data science, is a field that can be learned by anyone with the right resources and dedication, and there are many online courses, books, and tutorials available to help individuals learn the necessary skills and one of the best platform to learn Data Science is TalentServe. Employment in the field of data science is projected to grow by 11% from 2019 to 2029, which is much faster than the average for all occupations, due to the increasing demand for data-driven insights in various industries. Data science encompasses various activities, including data collection, data cleaning and preprocessing, exploratory data analysis, feature engineering, model selection and evaluation, and communication of results. It also involves using machine learning algorithms, data visualization, and statistical analysis to make predictions and provide actionable insights based on data. For more, details visit our site:https://www.talentserve.org/course-datascience COURSE CONTENT Introduction to Python Basic Steps NUMPY Data Visualization Pandas Exceptions and Errors Introduction to Artificial Intelligence and Machine Learning Data Wrangling and Manipulation Supervised Learning Supervised Learning-Classification Unsupervised learning Machine Learning Pipeline Building Decision Tree Analysis and Ensemble Learning AI and Deep learning introduction Artificial Neural Network Deep Neural Network & Tools Deep Neural Net optimization, tuning, interpretability Convolutional Neural Net Recurrent Neural Networks Overfit and underfit Transfer Learning Working with Generative Adversarial Networks Pytorch C-103, 1st Floor, Shree Sai Tower, CHSL, Sodawala Lane, Near Sterling Hospital, Borivali (W), Mumbai-400092 hello@talentserve.org

Hadoop is about to get a lot less complicated. A number of the largest big data vendors, including IBM, Hortonworks and Pivotal, have joined forces to standardise the base platform for the open source software. It is hoped the Open Data Platform announced in February will reduce the work required on the part of enterprises to build and maintain complex Hadoop-based data analysis systems. Open source tools for big data: How Hadoop could transform your job and your business Learn Big Data from Big Data Solutions Architects! Hadoop Training Chennai with Hands-On Practical Approach ! Hadoop Training, Classroom, Online, Corporate Training in Chennai Visit Us: #67, 2nd Floor, Gandhi Nagar 1st Main Road, Adyar, Chennai-20 To register for upcoming classes weekends(Classroom) April - Fast Track (Classroom)- April

TekSlate’s syndicate of online trainers are experienced experts in knowledge transfer. The best place for learning, if you are aiming to learn the crux of any technology Big Data Green Plum DBA Online Training Course Content: Greenplum Architecture Greenplum Master Greenplum Segments Greenplum Interconnect Greenplum Concepts, Features and Benefits Redundancy and Failover in Greenplum Database Segment Mirroring Master Mirroring Data Distribution in greenplum Checking for Uneven Data Distribution Database Installation and Configuration Lab – Database Installation and Configuration Access Control and Security Managing Roles and Privileges Security Best Practices for Roles and Privileges Creating New Roles (Users) Altering Role Attributes Creating Groups (Role Membership) Managing Object Privileges Configuring Client Authentication Allowing Connections to Greenplum Database Editing the pg_hba.conf File Limiting Concurrent Connections Accessing the Database Establishing a Database Session Supported Client Applications Greenplum Database Client Applications PgAdmin III for Greenplum Database Database Application Interfaces Third-Party Client Tools Troubleshooting Connection Problems Lab Managing Workload and Resources Overview of Greenplum Workload Management How Resource Queues Work in Greenplum Database Steps to Enable Workload Management Configuring Workload Management Resource Queues Database Administration Defining Database Objects (databases, schema, table, sequence etc) Creating and using databaseso Creating and using schemas Creating and Using Sequences Indexes in Greenplum Database Creating and Managing Views Managing Data Vacuuming the Database Querying Data Query Performance EXPLAIN Output, EXPLAIN ANALYZE Output Partitioning Large Tables Understanding Table Partitioning in Greenplum Database Deciding on a Table Partitioning Strategy Creating Partitioned Tables Loading Partitioned Tables Maintaining Partitioned Tables Lab Greenplum Database Loading Tools Overview About External Tables About gpload About COPY Using the Greenplum Parallel File Server (gpfdist) System Administration Starting and Stopping Greenplum Routine admin tasks Uploading Configuration File Changes Only Configuring Your Greenplum System About Greenplum Master and Local Parameters Setting Configuration Parameters Setting a Local Configuration Parameter Setting a Master Configuration Parameter Enabling Alerts and Notifications Checking for Failed Segments Recovering a Failed Segment Recovering a Failed Master Backing up and Restoring Databases Backup and Restore Operations Expanding a Greenplum System Lab Monitoring a Greenplum System Monitoring Database Activity and Performance Monitoring System State Enabling System Alerts and Notifications Checking System Stateo Checking Disk Space Usage Routine System Maintenance Tasks Routine Vacuum and Analyze System Catalog Maintenance Managing Greenplum Database Log Files Performance Tuning Common Causes of Performance Issues Identifying Hardware and Segment Failures Managing Workload Maintaining Database Statistics Identifying Statistics Problems in Query Plans Tuning Statistics Collection Optimizing Data Distribution Optimizing Your Database Design Lab GP-development Functions Basic shell scripts Investigating a Performance Problem Checking System State Checking Database Activity Checking for Active Sessions (Workload) Checking for Locks (Contention) Checking Query Status and System Utilization Troubleshooting Problem Queries gp_toolkit administration schema System catalog Performance issues and tips Automation scripts for GPDB administration Lab Materials/tools used 1. Training ppts 2. Greenplum admin guide. 3. Greenplum installation guide. 4. Pg admin installer for windows. (Greenplum client for windows) 5. Greenplum community edition. 6. Putty 7. GP connectivity tools. Expected from audience (for Lab) 1. Laptop or desktop with centos installed. 2. Internet.

WARM WELCOME FROM VELGRO TECHNOLOGIES!!! We are providing training for DATA WAREHOUSE Training is provided by well experienced (REAL TIME) Faculty!!! Fee structure Course fees: RS./- Data Warehouse The Compelling Need for Data Warehousing • Escalating Need for Strategic Information • Failures of Past Decision-Support Systems • Operational Versus Decision-Support Systems • Datawarehousing—The Only Viable Solution • Datawarehouse Defined • The Datawarehousing Movement • Evolution of Business Intelligence Data Warehouse: The Building Blocks • Review formal definitions of a data warehouse • Discuss the defining features • Distinguish between data warehouses and data marts • Study each component or building block that makes up a data warehouse • Introduce metadata and highlight its significance Trends in Data Warehousing • Data Warehouse Do-Overs • Proliferation of Data Sources • Outsourcing • Hub Versus Relational Databases • Active Data Warehouses • Fusion with CRM • Growing Number of End Users • More Complex Queries • Integrated Customer View • Exploding Data Volumes Planning and Project Management • Phases in project management • Inception • Elaboration • Construction • Transition Defining the Business Requirements • To gain agreement with stakeholders • To provide a foundation to communicate to a technology service provider what the solution needs to do to satisfy the customer’s and business’ needs • To provide input into the next phase for this project • To describe what not how the customer/business needs will be met by the solution Requirements as the driving force for data warehousing • Understand why business requirements are the driving force • Discuss how requirements drive every development phase • Specifically learn how requirements influence data design • Review the impact of requirements on architecture • Note the special considerations for ETL and metadata • Examine how requirements shape information delivery The Architectural Components • Overall Architecture • Data Warehouse Database • Sourcing, Acquisition, Cleanup and Transformation Tools • Meta data • Data Marts • Data Warehouse Administration and Management Infrastructure as the Foundation for Data Warehousing • Understand the distinction between architecture and infrastructure • Find out how the data warehouse infrastructure supports its architecture • Gain an insight into the components of the physical infrastructure • Review hardware and operating systems for the data warehouse • Study parallel processing options as applicable to the data warehouse • Discuss the server options in detail • Learn how to select the DBMS • Review the types of tools needed for the data warehouse • Study the concept and use of data warehouse appliances The Significant Role of Metadata • Find out why metadata is so important • Understand who needs metadata and what types they need • Review metadata types by the three functional areas • Discuss business metadata and technical metadata in detail • Examine all the requirements metadata must satisfy • Understand the challenges for metadata management • Study options for providing metadata Principles of Dimensional Modelling • Clearly understand how the requirements definition determines data design • Introduce dimensional modelling and contrast it with entity-relationship modelling • Review the basics of the STAR schema • Find out what is inside the fact table and inside the dimension tables • Determine the advantages of the STAR schema for data warehouses Dimensional Modelling: Advanced Topics • Discuss and get a good grasp of slowly changing dimensions • Understand large dimensions and how to deal with them • Examine the snowflake schema in detail • Learn about aggregate tables and determine when to use them • Completely survey families of STARS and their application Data Extraction, Transformation, and Loading • Part I: When to build your data warehouse • Part II: Building a new schema • Part III: Location of your data warehouse The Physical Design Process in DWH • Distinguish between physical design and logical design as applicable to the data warehouse • Study the steps in the physical design process in detail • Understand physical design considerations and know the implications • Grasp the role of storage considerations in physical design • Examine indexing techniques for the data warehouse environment • Review and summarize all performance enhancement options • Matching Information to the Classes of Users OLAP in the Data Warehouse • OLAP (Online Analytical Processing) • Overview & Introduction • OLAP Cube • HISTORY of OLAP • OLAP Operations • DATAWAREHOUSE • ARCHITECHTURE Difference between OLAP & • OLTP & TYPES OF OLAP APPLICATIONS OF OLAP • Data Warehousing and the Web • Data Warehouse Deployment • Data Quality: A Key to Success • Data Mining Basics • Growth and Maintenance contact person: SUJITHA Venue Address: #st floor,Gandhi Road, Velachery Email Id:velgrotechnologies at gmail dot com info at velgrotechnologies dot com Website:www dot velgrotechnologies dot com