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blogwithme01 · 11 days ago

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Magarpatta: Pune's Premier Tech Epicenter and Lifestyle Hub

Magarpatta City in Pune stands as a testament to thoughtful urban planning, evolving from agricultural land into a sprawling, self-sufficient IT and residential integrated township. Situated in Hadapsar, it has become a beacon for technology companies and a coveted address for IT professionals, cementing Pune's position as a leading Indian IT destination. The remarkable growth of IT companies in Magarpatta over the last two decades is a direct result of its strategic design, robust infrastructure, and a vibrant community ecosystem.

Why Magarpatta Attracts the Tech World

The allure of Magarpatta for IT businesses stems from a combination of unique advantages:

Integrated Urban Model: Unlike traditional business parks, Magarpatta offers a holistic living and working environment. This "walk-to-work" concept, with residential complexes, commercial towers, educational institutions, healthcare facilities, and recreational avenues within the same perimeter, significantly enhances employee convenience and quality of life.

Cutting-Edge Infrastructure: The dedicated IT park, Cybercity, is a showcase of modern architecture and technological readiness. It provides Grade A office spaces, uninterrupted power supply, high-speed fiber optic connectivity, and robust security systems, creating an ideal operational environment for tech enterprises.

Sustainable and Green Environment: Magarpatta's commitment to sustainability is evident in its ample green spaces, landscaped gardens, and emphasis on eco-friendly practices. This focus on environmental well-being contributes to a healthier and more productive work atmosphere.

Access to Skilled Talent: Pune's strong academic foundation, with numerous engineering colleges and technical universities, ensures a steady stream of highly skilled IT professionals. This ready availability of talent is a crucial factor for companies looking to expand their operations.

Key Players in Magarpatta's Tech Landscape

Magarpatta's Cybercity is home to a diverse array of IT companies, ranging from global multinational corporations to agile startups:

Global Giants: Major international firms have established significant development and delivery centers here. These include Accenture, providing extensive consulting and digital transformation services; Capgemini, known for its expertise in cloud and digital solutions; HCL Technologies, a prominent global IT services provider; Tata Consultancy Services (TCS), a powerhouse in software development and IT consulting; Infosys, a leader in next-gen digital services; Cognizant, focusing on business and technology consulting; Amdocs, specializing in software for communication service providers; and Red Hat, a pioneer in open-source solutions.

Specialized Centers: Several companies have dedicated innovation or development centers, such as HSBC Software Development (India) Pvt. Ltd. for banking technology, John Deere Technology Centre for agricultural solutions, Eaton India Innovation Center for power management, SAP (Sybase) for enterprise software, and Teradata for data analytics platforms.

Emerging & Mid-Sized Firms: Magarpatta also fosters a dynamic ecosystem of smaller and mid-sized IT companies and startups. Firms like YASH Technologies, Mobikode Software, Veracity Software, Qualitas IT Pvt Ltd, and Xento Systems contribute significantly to the local economy and drive innovation in niche areas like AI, machine learning, cybersecurity, mobile app development, and digital marketing.

Work Culture and Lifestyle Benefits

The companies in Magarpatta often cultivate a progressive work environment characterized by innovation, collaboration, and continuous learning. Employees benefit from:

Competitive Compensation & Benefits: Attractive salary packages, performance incentives, and comprehensive benefits are standard.

Flexible Work Models: Many firms embrace hybrid and remote work options, promoting work-life balance.

Professional Development: Emphasis on upskilling, training programs, and certifications to keep pace with evolving technologies.

Convenient Lifestyle: Access to a plethora of amenities, including Seasons Mall, food courts, healthcare facilities, parks, and recreational options, all within close proximity. The safe and well-maintained environment enhances the overall living experience.

Magarpatta's Future as a Tech Powerhouse

Magarpatta's strategic advantages, combined with its established reputation, position it for continued growth. As technology continues its rapid evolution, the township's ability to attract diverse IT talent and foster a conducive business environment will remain key. Magarpatta is not just an IT hub; it's a thriving tech ecosystem where innovation flourishes, careers are built, and a high quality of life is genuinely attainable. Its success story serves as a blueprint for sustainable IT development in India.

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globalresourcesvn · 21 days ago

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Kiến thức về Linked Server trong SQL Server

Kiến thức về Linked Server trong SQL Server #SQLServer #LinkedServer #Database #KếtNốiDữLiệu #SQL Linked Server là một cấu hình mạnh mẽ trong SQL Server cho phép một phiên bản SQL Server (được gọi là máy chủ cục bộ hoặc Local Server) kết nối và truy vấn dữ liệu từ các nguồn dữ liệu khác nhau, bao gồm cả các máy chủ SQL Server khác, các cơ sở dữ liệu Oracle, DB2, Sybase, và nhiều nguồn dữ liệu…

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kamalkafir-blog · 28 days ago

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IT Technology Services Specialist - SAP Enterprise Cloud Services

Job title: IT Technology Services Specialist – SAP Enterprise Cloud Services Company: SAP Job description: by focussing on automation enhancements. Role Requirements: Excellent hands-on experience in one of the areas like SAP Basis…. Practical knowledge with SAP technologies like SAP NetWeaver, Business Objects, SAP HANA and SAP Sybase Adaptive Server… Expected salary: Location: Bangalore,…

#business automation consultant

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ralantechinnovate · 2 months ago

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Seamless Cross Database Migration with RalanTech

In today's rapidly evolving digital landscape, businesses must ensure their data management systems are both efficient and adaptable. Cross database migration has become a critical strategy for organizations aiming to upgrade their infrastructure, enhance performance, and reduce costs. RalanTech stands out as a leader in this domain, offering affordable database migration services and expert consulting to facilitate smooth transitions.

Understanding Cross Database Migration

Cross database migration involves transferring data between different database management systems (DBMS), such as moving from Oracle to PostgreSQL or from Sybase to SQL Server. This process is essential for organizations seeking to modernize their systems, improve scalability, or integrate new technologies. However, it requires meticulous planning and execution to maintain data integrity and minimize downtime.

The Importance of Affordable Database Migration Services

Cost is a significant consideration in any migration project. Affordable database migration services ensure that businesses of all sizes can access the benefits of modern DBMS without prohibitive expenses. RalanTech offers cost-effective solutions tailored to meet specific business needs, ensuring a high return on investment.

RalanTech's Expertise in Database Migration Consulting

With a team of seasoned professionals, RalanTech provides comprehensive database migration consulting services. Their approach includes assessing current systems, planning strategic migrations, and executing transitions with minimal disruption. By leveraging their expertise, businesses can navigate the complexities of migration confidently.

Why Choose RalanTech for Your Migration Needs?

Proven Track Record

RalanTech has successfully completed over 295 projects, demonstrating their capability and reliability in handling complex migration tasks.

Customized Solutions

Understanding that each business has unique requirements, RalanTech offers tailored migration strategies that align with specific goals and operational needs.

Comprehensive Support

From initial assessment to post-migration support, RalanTech ensures continuous assistance, addressing any challenges that arise during the migration process.

The Migration Process: A Step-by-Step Overview

Assessment and Planning: Evaluating the existing database environment to identify potential risks and develop a strategic migration plan.

Data Mapping and Extraction: Ensuring data compatibility and accurately extracting data from the source system.

Data Transformation and Loading: Converting data to fit the target system's structure and loading it efficiently.

Testing and Validation: Conducting thorough tests to verify data integrity and system functionality.

Deployment and Optimization: Implementing the new system and optimizing performance for seamless operation.

Post-Migration Support: Providing ongoing assistance to address any post-migration issues and ensure system stability.

Ensuring Data Integrity and Security

Maintaining data integrity and security is paramount during migration. RalanTech employs robust protocols to protect sensitive information and ensure compliance with industry standards.

Minimizing Downtime and Disruption

Understanding the importance of business continuity, RalanTech designs migration strategies that minimize downtime and operational disruption, allowing businesses to maintain productivity throughout the transition.

Scalability and Future-Proofing Your Database

RalanTech's migration solutions are designed with scalability in mind, enabling businesses to accommodate future growth and technological advancements seamlessly.

Leveraging Cloud Technologies

Migrating databases to the cloud offers enhanced flexibility and cost savings. RalanTech specializes in cloud migrations, facilitating transitions to platforms like AWS, Azure, and Google Cloud.

Industry-Specific Migration Solutions

RalanTech tailors its migration services to meet the unique demands of various industries, including healthcare, finance, and manufacturing, ensuring compliance and optimized performance.

Training and Empowering Your Team

Beyond technical migration, RalanTech offers training to internal teams, empowering them to manage and optimize the new database systems effectively.

Measuring Success: Post-Migration Metrics

RalanTech emphasizes the importance of post-migration evaluation, utilizing key performance indicators to assess the success of the migration and identify areas for further optimization.

Continuous Improvement and Support

Committed to long-term client success, RalanTech provides ongoing support and continuous improvement strategies to adapt to evolving business needs and technological landscapes.

#DataMigration#DigitalTransformation #CloudMigration #CloudComputing #CloudServices #BusinessTransformation #DatabaseMigration #DataManagement #ITConsulting

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fromdevcom · 2 months ago

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Java Database Connectivity API contains commonly asked Java interview questions. A good understanding of JDBC API is required to understand and leverage many powerful features of Java technology. Here are few important practical questions and answers which can be asked in a Core Java JDBC interview. Most of the java developers are required to use JDBC API in some type of application. Though its really common, not many people understand the real depth of this powerful java API. Dozens of relational databases are seamlessly connected using java due to the simplicity of this API. To name a few Oracle, MySQL, Postgres and MS SQL are some popular ones. This article is going to cover a lot of general questions and some of the really in-depth ones to. Java Interview Preparation Tips Part 0: Things You Must Know For a Java Interview Part 1: Core Java Interview Questions Part 2: JDBC Interview Questions Part 3: Collections Framework Interview Questions Part 4: Threading Interview Questions Part 5: Serialization Interview Questions Part 6: Classpath Related Questions Part 7: Java Architect Scalability Questions What are available drivers in JDBC? JDBC technology drivers fit into one of four categories: A JDBC-ODBC bridge provides JDBC API access via one or more ODBC drivers. Note that some ODBC native code and in many cases native database client code must be loaded on each client machine that uses this type of driver. Hence, this kind of driver is generally most appropriate when automatic installation and downloading of a Java technology application is not important. A native-API partly Java technology-enabled driver converts JDBC calls into calls on the client API for Oracle, Sybase, Informix, DB2, or other DBMS. Note that, like the bridge driver, this style of driver requires that some binary code be loaded on each client machine. A net-protocol fully Java technology-enabled driver translates JDBC API calls into a DBMS-independent net protocol which is then translated to a DBMS protocol by a server. This net server middleware is able to connect all of its Java technology-based clients to many different databases. The specific protocol used depends on the vendor. In general, this is the most flexible JDBC API alternative. It is likely that all vendors of this solution will provide products suitable for Intranet use. In order for these products to also support Internet access they must handle the additional requirements for security, access through firewalls, etc., that the Web imposes. Several vendors are adding JDBC technology-based drivers to their existing database middleware products. A native-protocol fully Java technology-enabled driver converts JDBC technology calls into the network protocol used by DBMSs directly. This allows a direct call from the client machine to the DBMS server and is a practical solution for Intranet access. Since many of these protocols are proprietary the database vendors themselves will be the primary source for this style of driver. Several database vendors have these in progress. What are the types of statements in JDBC? the JDBC API has 3 Interfaces, (1. Statement, 2. PreparedStatement, 3. CallableStatement ). The key features of these are as follows: Statement This interface is used for executing a static SQL statement and returning the results it produces. The object of Statement class can be created using Connection.createStatement() method. PreparedStatement A SQL statement is pre-compiled and stored in a PreparedStatement object. This object can then be used to efficiently execute this statement multiple times. The object of PreparedStatement class can be created using Connection.prepareStatement() method. This extends Statement interface. CallableStatement This interface is used to execute SQL stored procedures. This extends PreparedStatement interface. The object of CallableStatement class can be created using Connection.prepareCall() method.

What is a stored procedure? How to call stored procedure using JDBC API? Stored procedure is a group of SQL statements that forms a logical unit and performs a particular task. Stored Procedures are used to encapsulate a set of operations or queries to execute on database. Stored procedures can be compiled and executed with different parameters and results and may have any combination of input/output parameters. Stored procedures can be called using CallableStatement class in JDBC API. Below code snippet shows how this can be achieved. CallableStatement cs = con.prepareCall("call MY_STORED_PROC_NAME"); ResultSet rs = cs.executeQuery(); What is Connection pooling? What are the advantages of using a connection pool? Connection Pooling is a technique used for sharing the server resources among requested clients. It was pioneered by database vendors to allow multiple clients to share a cached set of connection objects that provides access to a database. Getting connection and disconnecting are costly operation, which affects the application performance, so we should avoid creating multiple connection during multiple database interactions. A pool contains set of Database connections which are already connected, and any client who wants to use it can take it from pool and when done with using it can be returned back to the pool. Apart from performance this also saves you resources as there may be limited database connections available for your application. How to do database connection using JDBC thin driver ? This is one of the most commonly asked questions from JDBC fundamentals, and knowing all the steps of JDBC connection is important. import java.sql.*; class JDBCTest public static void main (String args []) throws Exception //Load driver class Class.forName ("oracle.jdbc.driver.OracleDriver"); //Create connection Connection conn = DriverManager.getConnection ("jdbc:oracle:thin:@hostname:1526:testdb", "scott", "tiger"); // @machineName:port:SID, userid, password Statement stmt = conn.createStatement(); ResultSet rs = stmt.executeQuery("select 'Hi' from dual"); while (rs.next()) System.out.println (rs.getString(1)); // Print col 1 => Hi stmt.close(); What does Class.forName() method do? Method forName() is a static method of java.lang.Class. This can be used to dynamically load a class at run-time. Class.forName() loads the class if its not already loaded. It also executes the static block of loaded class. Then this method returns an instance of the loaded class. So a call to Class.forName('MyClass') is going to do following - Load the class MyClass. - Execute any static block code of MyClass. - Return an instance of MyClass. JDBC Driver loading using Class.forName is a good example of best use of this method. The driver loading is done like this Class.forName("org.mysql.Driver"); All JDBC Drivers have a static block that registers itself with DriverManager and DriverManager has static initializer method registerDriver() which can be called in a static blocks of Driver class. A MySQL JDBC Driver has a static initializer which looks like this: static try java.sql.DriverManager.registerDriver(new Driver()); catch (SQLException E) throw new RuntimeException("Can't register driver!"); Class.forName() loads driver class and executes the static block and the Driver registers itself with the DriverManager. Which one will you use Statement or PreparedStatement? Or Which one to use when (Statement/PreparedStatement)? Compare PreparedStatement vs Statement. By Java API definitions: Statement is a object used for executing a static SQL statement and returning the results it produces. PreparedStatement is a SQL statement which is precompiled and stored in a PreparedStatement object. This object can then be used to efficiently execute this statement multiple times. There are few advantages of using PreparedStatements over Statements

Since its pre-compiled, Executing the same query multiple times in loop, binding different parameter values each time is faster. (What does pre-compiled statement means? The prepared statement(pre-compiled) concept is not specific to Java, it is a database concept. Statement precompiling means: when you execute a SQL query, database server will prepare a execution plan before executing the actual query, this execution plan will be cached at database server for further execution.) In PreparedStatement the setDate()/setString() methods can be used to escape dates and strings properly, in a database-independent way. SQL injection attacks on a system are virtually impossible when using PreparedStatements. What does setAutoCommit(false) do? A JDBC connection is created in auto-commit mode by default. This means that each individual SQL statement is treated as a transaction and will be automatically committed as soon as it is executed. If you require two or more statements to be grouped into a transaction then you need to disable auto-commit mode using below command con.setAutoCommit(false); Once auto-commit mode is disabled, no SQL statements will be committed until you explicitly call the commit method. A Simple transaction with use of autocommit flag is demonstrated below. con.setAutoCommit(false); PreparedStatement updateStmt = con.prepareStatement( "UPDATE EMPLOYEE SET SALARY = ? WHERE EMP_NAME LIKE ?"); updateStmt.setInt(1, 5000); updateSales.setString(2, "Jack"); updateStmt.executeUpdate(); updateStmt.setInt(1, 6000); updateSales.setString(2, "Tom"); updateStmt.executeUpdate(); con.commit(); con.setAutoCommit(true); What are database warnings and How can I handle database warnings in JDBC? Warnings are issued by database to notify user of a problem which may not be very severe. Database warnings do not stop the execution of SQL statements. In JDBC SQLWarning is an exception that provides information on database access warnings. Warnings are silently chained to the object whose method caused it to be reported. Warnings may be retrieved from Connection, Statement, and ResultSet objects. Handling SQLWarning from connection object //Retrieving warning from connection object SQLWarning warning = conn.getWarnings(); //Retrieving next warning from warning object itself SQLWarning nextWarning = warning.getNextWarning(); //Clear all warnings reported for this Connection object. conn.clearWarnings(); Handling SQLWarning from Statement object //Retrieving warning from statement object stmt.getWarnings(); //Retrieving next warning from warning object itself SQLWarning nextWarning = warning.getNextWarning(); //Clear all warnings reported for this Statement object. stmt.clearWarnings(); Handling SQLWarning from ResultSet object //Retrieving warning from resultset object rs.getWarnings(); //Retrieving next warning from warning object itself SQLWarning nextWarning = warning.getNextWarning(); //Clear all warnings reported for this resultset object. rs.clearWarnings(); The call to getWarnings() method in any of above way retrieves the first warning reported by calls on this object. If there is more than one warning, subsequent warnings will be chained to the first one and can be retrieved by calling the method SQLWarning.getNextWarning on the warning that was retrieved previously. A call to clearWarnings() method clears all warnings reported for this object. After a call to this method, the method getWarnings returns null until a new warning is reported for this object. Trying to call getWarning() on a connection after it has been closed will cause an SQLException to be thrown. Similarly, trying to retrieve a warning on a statement after it has been closed or on a result set after it has been closed will cause an SQLException to be thrown. Note that closing a statement also closes a result set that it might have produced. What is Metadata and why should I use it?

JDBC API has 2 Metadata interfaces DatabaseMetaData & ResultSetMetaData. The DatabaseMetaData provides Comprehensive information about the database as a whole. This interface is implemented by driver vendors to let users know the capabilities of a Database Management System (DBMS) in combination with the driver based on JDBC technology ("JDBC driver") that is used with it. Below is a sample code which demonstrates how we can use the DatabaseMetaData DatabaseMetaData md = conn.getMetaData(); System.out.println("Database Name: " + md.getDatabaseProductName()); System.out.println("Database Version: " + md.getDatabaseProductVersion()); System.out.println("Driver Name: " + md.getDriverName()); System.out.println("Driver Version: " + md.getDriverVersion()); The ResultSetMetaData is an object that can be used to get information about the types and properties of the columns in a ResultSet object. Use DatabaseMetaData to find information about your database, such as its capabilities and structure. Use ResultSetMetaData to find information about the results of an SQL query, such as size and types of columns. Below a sample code which demonstrates how we can use the ResultSetMetaData ResultSet rs = stmt.executeQuery("SELECT a, b, c FROM TABLE2"); ResultSetMetaData rsmd = rs.getMetaData(); int numberOfColumns = rsmd.getColumnCount(); boolean b = rsmd.isSearchable(1); What is RowSet? or What is the difference between RowSet and ResultSet? or Why do we need RowSet? or What are the advantages of using RowSet over ResultSet? RowSet is a interface that adds support to the JDBC API for the JavaBeans component model. A rowset, which can be used as a JavaBeans component in a visual Bean development environment, can be created and configured at design time and executed at run time. The RowSet interface provides a set of JavaBeans properties that allow a RowSet instance to be configured to connect to a JDBC data source and read some data from the data source. A group of setter methods (setInt, setBytes, setString, and so on) provide a way to pass input parameters to a rowset's command property. This command is the SQL query the rowset uses when it gets its data from a relational database, which is generally the case. Rowsets are easy to use since the RowSet interface extends the standard java.sql.ResultSet interface so it has all the methods of ResultSet. There are two clear advantages of using RowSet over ResultSet RowSet makes it possible to use the ResultSet object as a JavaBeans component. As a consequence, a result set can, for example, be a component in a Swing application. RowSet be used to make a ResultSet object scrollable and updatable. All RowSet objects are by default scrollable and updatable. If the driver and database being used do not support scrolling and/or updating of result sets, an application can populate a RowSet object implementation (e.g. JdbcRowSet) with the data of a ResultSet object and then operate on the RowSet object as if it were the ResultSet object. What is a connected RowSet? or What is the difference between connected RowSet and disconnected RowSet? or Connected vs Disconnected RowSet, which one should I use and when? Connected RowSet A RowSet object may make a connection with a data source and maintain that connection throughout its life cycle, in which case it is called a connected rowset. A rowset may also make a connection with a data source, get data from it, and then close the connection. Such a rowset is called a disconnected rowset. A disconnected rowset may make changes to its data while it is disconnected and then send the changes back to the original source of the data, but it must reestablish a connection to do so. Example of Connected RowSet: A JdbcRowSet object is a example of connected RowSet, which means it continually maintains its connection to a database using a JDBC technology-enabled driver. Disconnected RowSet A disconnected rowset may have a reader (a RowSetReader object) and a writer (a RowSetWriter object) associated with it.

The reader may be implemented in many different ways to populate a rowset with data, including getting data from a non-relational data source. The writer can also be implemented in many different ways to propagate changes made to the rowset's data back to the underlying data source. Example of Disconnected RowSet: A CachedRowSet object is a example of disconnected rowset, which means that it makes use of a connection to its data source only briefly. It connects to its data source while it is reading data to populate itself with rows and again while it is propagating changes back to its underlying data source. The rest of the time, a CachedRowSet object is disconnected, including while its data is being modified. Being disconnected makes a RowSet object much leaner and therefore much easier to pass to another component. For example, a disconnected RowSet object can be serialized and passed over the wire to a thin client such as a personal digital assistant (PDA). What is the benefit of having JdbcRowSet implementation? Why do we need a JdbcRowSet like wrapper around ResultSet? The JdbcRowSet implementation is a wrapper around a ResultSet object that has following advantages over ResultSet This implementation makes it possible to use the ResultSet object as a JavaBeans component. A JdbcRowSet can be used as a JavaBeans component in a visual Bean development environment, can be created and configured at design time and executed at run time. It can be used to make a ResultSet object scrollable and updatable. All RowSet objects are by default scrollable and updatable. If the driver and database being used do not support scrolling and/or updating of result sets, an application can populate a JdbcRowSet object with the data of a ResultSet object and then operate on the JdbcRowSet object as if it were the ResultSet object. Can you think of a questions which is not part of this post? Please don't forget to share it with me in comments section & I will try to include it in the list.

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intellimindzblog · 8 months ago

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Navigating Sybase: Your Guide to Efficient Data Management

Sybase is designed for database professionals, developers, and analysts seeking to enhance their expertise in Sybase, one of the leading relational database management systems.

In this training, you will:

Understand Sybase Architecture: Explore the core components of Sybase, including its database structures and how they interact with applications.

Learn Data Management Best Practices: Discover techniques for efficient data storage, retrieval, and manipulation, ensuring optimal performance.

Optimize Queries and Performance: Gain insights into writing efficient SQL queries and using performance tuning tools to enhance database efficiency.

Implement Security Measures: Understand the importance of data security and learn how to implement robust security protocols to protect sensitive information.

Utilize Sybase for Business Intelligence: Learn how to leverage Sybase tools for analytics and reporting, turning raw data into actionable insights.

Through hands-on exercises, real-world examples, and interactive discussions, you will develop practical skills that can be applied immediately in your organization. Whether you’re new to Sybase or looking to deepen your knowledge, this training will empower you to manage data effectively and make informed decisions.

Elevate your database management skills and unlock the full potential of Sybase.

#sybase #onlinecourse #learn

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aitoolswhitehattoolbox · 7 months ago

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Java Lead Developer

Job Description Infosys is seeking a Java Lead Developer. This position’s primary responsibility will be to translate… and analysis in Core Java, J2EE, Spring, any RDBMS (Oracle/Sybase/DB2/SQL Server/Postgresql). Working knowledge of Unix/Linux… Apply Now

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ralantech2 · 1 year ago

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RalanTech: Expert Sybase Remote DBA Support https://www.ralantech.com/sybase-database-support/ Gain peace of mind with RalanTech's specialized Sybase Remote DBA Support. Our experienced team ensures optimal performance, reliability, and security for your Sybase databases, allowing you to focus on your core business activities.

#sybase remote dba support

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craftywombatkingdom · 8 months ago

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sybase

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jcmarchi · 8 months ago

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Babak Hodjat, CTO of AI at Cognizant – Interview Series

New Post has been published on https://thedigitalinsider.com/babak-hodjat-cto-of-ai-at-cognizant-interview-series/

Babak Hodjat, CTO of AI at Cognizant – Interview Series

Babak Hodjat is Vice President of Evolutionary AI at Cognizant, and former co-founder and CEO of Sentient. He is responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world’s first AI-driven hedge fund, Sentient Investment Management. He is a serial entrepreneur, having started a number of Silicon Valley companies as main inventor and technologist.

Prior to co-founding Sentient, Babak was senior director of engineering at Sybase iAnywhere, where he led mobile solutions engineering. He was also co-founder, CTO and board member of Dejima Inc. Babak is the primary inventor of Dejima’s patented, agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – the technology behind Apple’s Siri.

A published scholar in the fields of artificial life, agent-oriented software engineering and distributed artificial intelligence, Babak has 31 granted or pending patents to his name. He is an expert in numerous fields of AI, including natural language processing, machine learning, genetic algorithms and distributed AI and has founded multiple companies in these areas. Babak holds a Ph.D. in machine intelligence from Kyushu University, in Fukuoka, Japan.

Looking back at your career, from founding multiple AI-driven companies to leading Cognizant’s AI Lab, what are the most important lessons you’ve learned about innovation and leadership in AI?

Innovation needs patience, investment, and nurturing, and it should be fostered and unrestricted. If you’ve built the right team of innovators, you can trust them and give them full artistic freedom to choose how and what they research. The results will often amaze you. From a leadership perspective, research and innovation should not be a nice-to-have or an afterthought. I’ve set up research teams pretty early on when building start-ups and have always been a strong advocate of research investment, and it has paid off. In good times, research keeps you ahead of competition, and in bad times, it helps you diversify and survive, so there is no excuse for underinvesting, restricting or overburdening it with short-term business priorities.

As one of the primary inventors of Apple’s Siri, how has your experience with developing intelligent interfaces shaped your approach to leading AI initiatives at Cognizant?

The natural language technology I originally developed for Siri was agent-based, so I have been working with the concept for a long time. AI wasn’t as powerful in the ’90s, so I used a multi-agent system to tackle understanding and mapping of natural language commands to actions. Each agent represented a small subset of the domain of discourse, so the AI in each agent had a simple environment to master. Today, AI systems are powerful, and one LLM can do many things, but we still benefit by treating it as a knowledge worker in a box, restricting its domain, giving it a job description and linking it to other agents with different responsibilities. The AI is thus able to augment and improve any business workflow.

As part of my remit as CTO of AI at Cognizant, I run our Advanced AI Lab in San Francisco. Our core research principle is agent-based decision-making. As of today, we currently have 56 U.S. patents on core AI technology based on that principle. We’re all in.

Could you elaborate on the cutting-edge research and innovations currently being developed at Cognizant’s AI Lab? How are these developments addressing the specific needs of Fortune 500 companies?

We have several AI studios and innovation centers. Our Advanced AI Lab in San Francisco focuses on extending the state of the art in AI. This is part of our commitment announced last year to invest $1 billion in generative AI over the next three years.

More specifically, we’re focused on developing new algorithms and technologies to serve our clients. Trust, explainability and multi-objective decisions are among the important areas we’re pursuing that are vital for Fortune 500 enterprises.

Around trust, we’re interested in research and development that deepens our understanding of when we can trust AI’s decision-making enough to defer to it, and when a human should get involved. We have several patents related to this type of uncertainty modeling. Similarly, neural networks, generative AI and LLMs are inherently opaque. We want to be able to evaluate an AI decision and ask it questions about why it recommended something – essentially making it explainable. Finally, we understand that sometimes, decisions companies want to be able to make have more than one outcome objective—cost reduction while increasing revenues balanced with ethical considerations, for example. AI can help us achieve the best balance of all of these outcomes by optimizing decision strategies in a multi-objective manner. This is another very important area in our AI research.

The next two years are considered critical for generative AI. What do you believe will be the pivotal changes in this period, and how should enterprises prepare?

We’re heading into an explosive period for the commercialization of AI technologies. Today, AI’s primary uses are improving productivity, creating better natural language-driven user interfaces, summarizing data and helping with coding. During this acceleration period, we believe that organizing overall technology and AI strategies around the core tenet of multi-agent systems and decision-making will best enable enterprises to succeed. At Cognizant, our emphasis on innovation and applied research will help our clients leverage AI to increase strategic advantage as it becomes further integrated into business processes.

How will Generative AI reshape industries, and what are the most exciting use cases emerging from Cognizant’s AI Lab?

Generative AI has been a huge step forward for businesses. You now have the ability to create a series of knowledge workers that can assist humans in their day-to-day work. Whether it’s streamlining customer service through intelligent chatbots or managing warehouse inventory through a natural language interface, LLMs are very good at specialized tasks.

But what comes next is what will truly reshape industries, as agents get the ability to communicate with each other. The future will be about companies having agents in their devices and applications that can address your needs and interact with other agents on your behalf. They will work across entire businesses to assist humans in every role, from HR and finance to marketing and sales. In the near future, businesses will gravitate naturally towards becoming agent-based.

Notably, we already have a multi-agent system that was developed in our lab in the form of Neuro AI, an AI use case generator that allows clients to rapidly build and prototype AI decisioning use cases for their business. It is already delivering some exciting results, and we’ll be sharing more on this soon.

What role will multi-agent architectures play in the next wave of Gen AI transformation, particularly in large-scale enterprise environments?

In our research and conversations with corporate leaders, we’re getting more and more questions about how they can make Generative AI impactful at scale. We believe the transformative promise of multi-agent artificial intelligence systems is central to achieving that impact. A multi-agent AI system brings together AI agents built into software systems in various areas across the enterprise. Think of it as a system of systems that allows LLMs to interact with one another. Today, the challenge is that, even though business objectives, activities, and metrics are deeply interwoven, the software systems used by disparate teams are not, creating problems. For example, supply chain delays can affect distribution center staffing. Onboarding a new vendor can impact Scope 3 emissions. Customer turnover could indicate product deficiencies. Siloed systems mean actions are often based on insights drawn from merely one program and applied to one function. Multi-agent architectures will light up insights and integrated action across the business. That’s real power that can catalyze enterprise transformation.

In what ways do you see multi-agent systems (MAS) evolving in the next few years, and how will this impact the broader AI landscape?

A multi-agent AI system functions as a virtual working group, analyzing prompts and drawing information from across the business to produce a comprehensive solution not just for the original requestor, but for other teams as well. If we zoom in and look at a particular industry, this could revolutionize operations in areas like manufacturing, for example. A Sourcing Agent would analyze existing processes and recommend more cost-effective alternative components based on seasons and demand. This Sourcing Agent would then connect with a Sustainability Agent to determine how the change would impact environmental goals. Finally, a Regulatory Agent would oversee compliance activity, ensuring teams submit complete, up-to-date reports on time.

The good news is many companies have already begun to organically integrate LLM-powered chatbots, but they need to be intentional about how they start to connect these interfaces. Care must be taken as to the granularity of agentification, the types of LLMs being used, and when and how to fine-tune them to make them effective. Organizations should start from the top, consider their needs and goals, and work down from there to decide what can be agentified.

What are the main challenges holding enterprises back from fully embracing AI, and how does Cognizant address these obstacles?

Despite leadership’s backing and investment, many enterprises fear falling behind on AI. According to our research, there’s a gap between leaders’ strategic commitment and the confidence to execute well. Cost and availability of talent and the perceived immaturity of current Gen AI solutions are two significant inhibitors holding enterprises back from fully embracing AI.

Cognizant plays an integral role helping enterprises traverse the AI productivity-to-growth journey. In fact, recent data from a study we conducted with Oxford Economics points to the need for outside expertise to help with AI adoption, with 43% of companies indicating they plan to work with external consultants to develop a plan for generative AI. Traditionally, Cognizant has owned the last mile with clients – we did this with data storage and cloud migration, and agentification will be no different. This is work that must be highly customized. It’s not a one size fits all journey. We’re the experts who can help identify the business goals and implementation plan, and then bring in the right custom-built agents to address business needs. We are, and have always been, the people to call.

Many companies struggle to see immediate ROI from their AI investments. What common mistakes do they make, and how can these be avoided?

Generative AI is far more effective when companies bring it into their own data context—that is to say, customize it on their own strong foundation of enterprise data. Also, sooner or later, enterprises will have to take the challenging step to reimagine their fundamental business processes. Today, many companies are using AI to automate and improve existing processes. Bigger results can happen when they start to ask questions like, what are the constituents of this process, how do I change them, and prepare for the emergence of something that doesn’t exist yet? Yes, this will necessitate a culture change and accepting some risk, but it seems inevitable when orchestrating the many parts of the organization into one powerful whole.

What advice would you give to emerging AI leaders who are looking to make a significant impact in the field, especially within large enterprises?

Business transformation is complex by nature. Emerging AI leaders within larger enterprises should focus on breaking down processes, experimenting with changes, and innovating. This requires a shift in mindset and calculated risks, but it can create a more powerful organization.

Thank you for the great interview, readers who wish to learn more should visit Cognizant.

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