Java applications cannot directly communicate with a database to submit data and retrieve the results of queries.
This is because a database can interpret only SQL statements and not Java language statements.
For this reason, you need a mechanism to translate Java statements into SQL statements.
The JDBC architecture provides the mechanism for this kind of translation.
The JDBC architecture can be classified into two layers :
JDBC application layer.
JDBC driver layer.
JDBC application layer : Signifies a Java application that uses the JDBC API to interact with the JDBC drivers. A JDBC driver is software that a Java application uses to access a database. The JDBC driver manager of JDBC API connects the Java application to the driver.
JDBC driver layer : Acts as an interface between a Java applications and a database. This layer contains a driver , such as a SQL server driver or an Oracle driver , which enables connectivity to a database.
A driver sends the request of a Java application to the database. After processing the request, the database sends the response back to the driver. The driver translates and sends the response to the JDBC API. The JDBC API forwards it to the Java application.
JDBC stands for Java Database Connectivity. JDBC is a Java API to connect and execute the query with the database. It is a part of JavaSE (Java Standard Edition). JDBC API uses JDBC drivers to connect with the database
Before JDBC, ODBC API was the database API to connect and execute the query with the database. But, ODBC API uses ODBC driver which is written in C language (i.e. platform dependent and unsecured). That is why Java has defined its own API (JDBC API) that uses JDBC drivers (written in Java language).
We can use JDBC API to handle database using Java program and can perform the following activities:
Connect to the database
Execute queries and update statements to the database
Retrieve the result received from the database.
JDBC : Java Database Connectivity
JDBC is used to connect java application with database.
JDBC is an API used to communicate Java application to database in database independent and platform independent manner.
It provides classes and interfaces to connect or communicate Java application with database.
Java applications cannot directly communicate with a database to submit data and retrieve the results of queries.
This is because a database can interpret only SQL statements and not Java language statements.
For this reason, you need a mechanism to translate Java statements into SQL statements.
The JDBC architecture provides the mechanism for this kind of translation.
The JDBC architecture can be classified into two layers :
JDBC application layer.
JDBC driver layer.
JDBC application layer : Signifies a Java application that uses the JDBC API to interact with the JDBC drivers. A JDBC driver is software that a Java application uses to access a database. The JDBC driver manager of JDBC API connects the Java application to the driver.
JDBC driver layer : Acts as an interface between a Java applications and a database. This layer contains a driver , such as a SQL server driver or an Oracle driver , which enables connectivity to a database.
A driver sends the request of a Java application to the database. After processing the request, the database sends the response back to the driver. The driver translates and sends the response to the JDBC API. The JDBC API forwards it to the Java application.
JDBC stands for Java Database Connectivity. JDBC is a Java API to connect and execute the query with the database. It is a part of JavaSE (Java Standard Edition). JDBC API uses JDBC drivers to connect with the database
Before JDBC, ODBC API was the database API to connect and execute the query with the database. But, ODBC API uses ODBC driver which is written in C language (i.e. platform dependent and unsecured). That is why Java has defined its own API (JDBC API) that uses JDBC drivers (written in Java language).
We can use JDBC API to handle database using Java program and can perform the following activities:
Connect to the database
Execute queries and update statements to the database
Retrieve the result received from the database.
JDBC : Java Database Connectivity
JDBC is used to connect java application with database.
JDBC is an API used to communicate Java application to database in database independent and platform independent manner.
It provides classes and interfaces to connect or communicate Java application with database.
Mumbai Academics is Mumbai’s first dedicated Professional Training Center for Training with Spoke and hub model with Multiple verticles . The strong foundation of Mumbai Academics is laid by highly skilled and trained Professionals, carrying mission to provide industry level input to the freshers and highly skilled and trained Software Professionals/other professional to IT companies.
JDBC tutorial with full example, including CRUD sql statement with JDBC Statement and PreparedStatement, interact Stored Procedure with CallableStatement, JDBC transaction
Mumbai Academics is Mumbai’s first dedicated Professional Training Center for Training with Spoke and hub model with Multiple verticles . The strong foundation of Mumbai Academics is laid by highly skilled and trained Professionals, carrying mission to provide industry level input to the freshers and highly skilled and trained Software Professionals/other professional to IT companies.
JDBC tutorial with full example, including CRUD sql statement with JDBC Statement and PreparedStatement, interact Stored Procedure with CallableStatement, JDBC transaction
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
3. JDBC Two TierArchitecture
• Java Application talks
directly to the database.
• Accomplished through the
JDBC driver which sends
commands directly to the
database.
• Results sent back directly to
the application
Application Space
Java Application
JDBC Driver
Database
SQL
Command
Result
Set
4. JDBC Three Tier Architecture
• JDBC driver sends
commands to a middle
tier, which in turn sends
commands to database.
• Results are sent back to
the middle tier, which
communicates them back
to the application
Application Space
Java Application
JDBC Driver
Database
SQL
Command
Result
Set
Application Server
(middle-tier)
Proprietary
Protocol
5. The JDBC API
The JDBC API stands for Java Database Connectivity Application Programming Interface. It
allows an application written in java to communicate and interacts with database.
It allows JAVA application to:
1) Create and open connection with database.
2) Specify and executes various SQL queries against database.
3) Retrieve records from database.
The JDBC API defines various classes and interfaces to communicate with database.
The JDBC classes are defined inside java.sql package.
6. JDBC Components
Interface Purpose
Driver Is used to create a connection object using connect()
method.
Connection Is used to monitor and maintain database sessions.
createStatement() method is used create statement.
Statement Is used to execute SQL statements and retrieve records
from database.
ResultSet Is used to retrieve records that are returned by
executing SQL query.
1) The java.sql package :
The java.sql package contains set of classes and interfaces that are used to
communicate with database.
Following are most common interfaces of java.sql package.
7. JDBC Components
Class Purpose
DriverManager Is used to manage multiple drivers. And also used to
load and register the JDBC drivers and establish
connection with database. The getconnection() method
of DriverManager class is used to create connection
object.
SQLException This class handles any errors that occur in a database
application.
Following are most common classes of java.sql package.
8. JDBC Components
2) JDBC Test Suite:
The JDBC driver test suite helps you to determine that JDBC drivers will run your program. These
tests are not comprehensive or exhaustive, but they do exercise many of the important features in
the JDBC API.
3) JDBC-ODBC Bridge :
The Java Software bridge provides JDBC access via ODBC drivers. Note that you need to load
ODBC binary code onto each client machine that uses this driver. As a result, the ODBC driver is
most appropriate on a corporate network where client installations are not a major problem, or for
application server code written in Java in a three-tier architecture.
9. JDBC-ODBC Bridge
Advantages Of JDBC.
o Can read any database.
o Creates XML structure of data from database.
o No content conversion
o Query and stored procedure supported.
Disadvantages Of JDBC.
o Not good for large project.
o It needs specific database queries.
o Multiple connections may have complexities
o Exception handling is a big issue with JDBC.
11. JDBC Drivers
JDBC Driver is a software component that enables java application to interact with the
database.
To help you understand what different drivers require, the following driver categorization
system id defined :-
o Type 1: JDBC-ODBC Bridge driver (Bridge).
o Type 2: Native-API/partly Java driver (Native).
o Type 3: All Java/Net-protocol driver (Middleware).
o Type 4: All Java/Native-protocol driver (Pure).
12.
13.
14.
15. Type2: Native-API,PartlyJavaDriver
• Native-API driver converts
JDBC commands into
DBMS-specific native calls
• Directly interfaces with the
database
Application Space
Java Application
Type 2 JDBC Driver
Database
SQL
Command
Result
Set
Native Database
Library
Proprietary
Protocol
16.
17.
18. Type4: Native-Protocol,PureJavaDriver
Pure Java drivers that
communicate directly with the
vendor’s database
JDBC commands converted to
database engine’s native protocol
directly
Advantage: no additional
translation or middleware layer
Improves performance
Application Space
Java Application
Type 4 JDBC Driver
Database
SQL Command
Using Proprietary
Protocol
Result Set
Using Proprietary
Protocol
19. Step-1 : Import JAVA SQl statement.
o import.java.sql.*;
Creating Database
Step-2 : Load and Register JDBC driver.
o Syntax : Class.forName (“Driver Name”);
Step-3 : Establish Connection with Database.
o Syntax : Connection conn= DriverManager.getConnection (“URL”, “Username”, ”Password”);
Step-4 : Create Statement.
o Statement stmt = conn.createstatement();
20. Step-5 : Execute Query.
o ResultSet rs= stmt.executeQuery("SELECT * FROM STUDENT");
o stmt.executeUpdate("INSERT INTO STUDENT VALUES(7,'abc','Chennai')”);
Step-6 : Retrieve Results (applied for select query)
o while(rs.next())
{
int id = rs.getInt("enroll");
String name= rs.getString("name");
String city= rs.getString("city");
System.out.println(id+"tt");
System.out.println(name+"tt");
System.out.println(city+"tt");
}
Step-7 : Closing Connection and Statement.
o conn.close();
ostmt.close();
Continued…..
21. // Step-1 : Import java.sql package
import java.sql.*;
public class database
{
public static void main(String args[])
{
Connection conn= null;
Statement stmt= null;
try
{
//Step-2: Load and register the JDBC driver
Class.forName("com.mysql.jdbc.Driver");
//Step-3 : Establish connection with Database.
System.out.println("Trying to connect with Database");
conn= DriverManager.getConnection("jdbc:mysql://localhost/","root","");
System.out.println("Connection Established Successfully");
//Step-4 : Create Statement.
System.out.println("Trying to create Database");
23. import java.sql.*;
public class dbpreparestmt
{
public static void main(String args[])
{
Connection conn = null;
Statement stmt = null;
try
{
Class.forName("com.mysql.jdbc.Driver");
System.out.println("Trying to connect with Database");
conn=DriverManager.getConnection("jdbc:mysql://localhost/jdemo","root","");
System.out.println("Connection Established Successfully");
System.out.println("Trying to insert data in table");
stmt = conn.createStatement();
PreparedStatement pst=conn.prepareStatement("INSERT INTO dhyey
VALUES(?,?,?)");
Insertion Using PrepareStatement