Metabase

Metabase is the easy, open source way for everyone in your company.

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About Metabase
Metabase is bringing data tools with the elegance and simplicity of consumer products to the crufty world of enterprise business intelligence. Our Open Source analytics and business intelligence application let installs in minutes, and can connect to most commonly used datab...
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Metabase
Metabase is the easy, open source way for everyone in your company.
0.00/5 (0 Reviews)
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When you buy a product on Amazon or book a flight through some airline’s application, you are exposed to their underlying data management system. A robust database system stores data securely and transmit them as per the user's request/query. With millions of data exchanged over the database interface, the complexity cannot be ignored. A database management system like PostgreSQL overcome this challenge. It empowers the business process to interconnect with each other seamlessly and complete the transaction successfully. ( Image source: Udemy) PostgreSQL is free and open-source software. It is the first database management system that implements a multi-version concurrency control (MVCC) feature, even before Oracle. It enables you to add custom functions developed using different programming languages such as C/C++, Java, etc. Their relational data management system has great advantages on traditional DBMS. Check the difference below. Difference between DBMS and RDBMS Features one must consider before choosing the database system for a business operation. Database size Deployment environment (Single Server, Distributed, Cloud etc.) Data security requirements Support of Advanced features like Scalability, Replication etc. Availability of technical support Management tools available There are other alternatives to PostgreSQL that could fit into your business model, depending on the size of your business. But before jumping straight to PostgreSQL alternative, check some of the leading data management system of 2019. Top 10 Database Management System of 2019 (Image source: db-engines) Top 10 Alternative to PostgreSQL MySQL: MySQL follows a client /server architecture. It is flexible and allows transactions to be rolled back, commits, and crash recovery. MySQL uses Triggers, Stored procedures and views, which enables the developer to give a higher productivity. MongoDB: It has an automatic load balancing configuration because of data placed in shards. It provides ad-hoc query support, which makes it exclusive. It can also index any field in a document. MariaDB:MariaDB offers many operations and commands unavailable in MySQL. It can run on a number of operating systems and supports a wide variety of programming languages. Microsoft SQL Server: Big data clusters are new additions to the SQL server 2019 release."Columnstore Indexes" feature introduced to reduce memory utilization on large queries. Teradata: Based on the concept "Shared Nothing design" Teradata contains a huge data processing system.Teradata supports ad-hoc queries. Apache Cassandra: Apache Cassandra is a highly scalable and manages high-velocity structured data across multiple commodity servers without a single point of failure.It performs blazingly fast writes and can store hundreds of terabytes of data, without sacrificing the read efficiency. Oracle Database: Oracle Database provides a comprehensive range of partitioning schemes to address every business requirement. Redis:The database is extremely fast. It loads up to 110,000 SETs/second and retrieves 81,000 GETs/second.Redis supports various types of data structures such as strings, hashes, sets, lists, sorted sets, etc. IBMD2:The storage optimization features of IBM Db2 can enhance performance, reduce elapsed time and significantly reduce processing power consumption Elaticsearch: It is highly scalable and runs perfectly fine on any machine or in a cluster containing hundreds of nodes. Below list compares the features of PostgreSQL alternatives.
When you buy a product on Amazon or book a flight through some airline’s application, you are exposed to their underlying data management system. A robust database system stores data securely and transmit them as per the user's request/query. With millions of data exchanged over the database interface, the complexity cannot be ignored. A database management system like PostgreSQL overcome this challenge. It empowers the business process to interconnect with each other seamlessly and complete the transaction successfully. ( Image source: Udemy) PostgreSQL is free and open-source software. It is the first database management system that implements a multi-version concurrency control (MVCC) feature, even before Oracle. It enables you to add custom functions developed using different programming languages such as C/C++, Java, etc. Their relational data management system has great advantages on traditional DBMS. Check the difference below. Difference between DBMS and RDBMS Features one must consider before choosing the database system for a business operation. Database size Deployment environment (Single Server, Distributed, Cloud etc.) Data security requirements Support of Advanced features like Scalability, Replication etc. Availability of technical support Management tools available There are other alternatives to PostgreSQL that could fit into your business model, depending on the size of your business. But before jumping straight to PostgreSQL alternative, check some of the leading data management system of 2019. Top 10 Database Management System of 2019 (Image source: db-engines) Top 10 Alternative to PostgreSQL MySQL: MySQL follows a client /server architecture. It is flexible and allows transactions to be rolled back, commits, and crash recovery. MySQL uses Triggers, Stored procedures and views, which enables the developer to give a higher productivity. MongoDB: It has an automatic load balancing configuration because of data placed in shards. It provides ad-hoc query support, which makes it exclusive. It can also index any field in a document. MariaDB:MariaDB offers many operations and commands unavailable in MySQL. It can run on a number of operating systems and supports a wide variety of programming languages. Microsoft SQL Server: Big data clusters are new additions to the SQL server 2019 release."Columnstore Indexes" feature introduced to reduce memory utilization on large queries. Teradata: Based on the concept "Shared Nothing design" Teradata contains a huge data processing system.Teradata supports ad-hoc queries. Apache Cassandra: Apache Cassandra is a highly scalable and manages high-velocity structured data across multiple commodity servers without a single point of failure.It performs blazingly fast writes and can store hundreds of terabytes of data, without sacrificing the read efficiency. Oracle Database: Oracle Database provides a comprehensive range of partitioning schemes to address every business requirement. Redis:The database is extremely fast. It loads up to 110,000 SETs/second and retrieves 81,000 GETs/second.Redis supports various types of data structures such as strings, hashes, sets, lists, sorted sets, etc. IBMD2:The storage optimization features of IBM Db2 can enhance performance, reduce elapsed time and significantly reduce processing power consumption Elaticsearch: It is highly scalable and runs perfectly fine on any machine or in a cluster containing hundreds of nodes. Below list compares the features of PostgreSQL alternatives.

When you buy a product on Amazon or book a flight through some airline’s application, you are exposed to their underlying data management system. A robust database system stores data securely and transmit them as per the user's request/query. With millions of data exchanged over the database interface, the complexity cannot be ignored.

A database management system like PostgreSQL overcome this challenge. It empowers the business process to interconnect with each other seamlessly and complete the transaction successfully.

( Image source: Udemy)

PostgreSQL is free and open-source software. It is the first database management system that implements a multi-version concurrency control (MVCC) feature, even before Oracle. It enables you to add custom functions developed using different programming languages such as C/C++, Java, etc. Their relational data management system has great advantages on traditional DBMS. Check the difference below.

Difference between DBMS and RDBMS

Features one must consider before choosing the database system for a business operation.

  • Database size
  • Deployment environment (Single Server, Distributed, Cloud etc.)
  • Data security requirements
  • Support of Advanced features like Scalability, Replication etc.
  • Availability of technical support
  • Management tools available

There are other alternatives to PostgreSQL that could fit into your business model, depending on the size of your business. But before jumping straight to PostgreSQL alternative, check some of the leading data management system of 2019.

Top 10 Database Management System of 2019

(Image source: db-engines)

Top 10 Alternative to PostgreSQL

  • MySQL: MySQL follows a client /server architecture. It is flexible and allows transactions to be rolled back, commits, and crash recovery. MySQL uses Triggers, Stored procedures and views, which enables the developer to give a higher productivity.
  • MongoDB: It has an automatic load balancing configuration because of data placed in shards. It provides ad-hoc query support, which makes it exclusive. It can also index any field in a document.
  • MariaDB:MariaDB offers many operations and commands unavailable in MySQL. It can run on a number of operating systems and supports a wide variety of programming languages.
  • Microsoft SQL Server: Big data clusters are new additions to the SQL server 2019 release."Columnstore Indexes" feature introduced to reduce memory utilization on large queries.
  • Teradata: Based on the concept "Shared Nothing design" Teradata contains a huge data processing system.Teradata supports ad-hoc queries.
  • Apache Cassandra: Apache Cassandra is a highly scalable and manages high-velocity structured data across multiple commodity servers without a single point of failure.It performs blazingly fast writes and can store hundreds of terabytes of data, without sacrificing the read efficiency.
  • Oracle Database: Oracle Database provides a comprehensive range of partitioning schemes to address every business requirement.
  • Redis:The database is extremely fast. It loads up to 110,000 SETs/second and retrieves 81,000 GETs/second.Redis supports various types of data structures such as strings, hashes, sets, lists, sorted sets, etc.
  • IBMD2:The storage optimization features of IBM Db2 can enhance performance, reduce elapsed time and significantly reduce processing power consumption
  • Elaticsearch: It is highly scalable and runs perfectly fine on any machine or in a cluster containing hundreds of nodes.

Below list compares the features of PostgreSQL alternatives.

RDBMS (Relational Database Management System) is always under the scanner in terms of its efficiency to handle Big Data, especially if it is unstructured data. Since the existence of both Big Data and RDBMS are evident, new technologies are developed for their peaceful co-existence.Greenplum database is one among them. What is the Greenplum Database?Greenplum Database is an open-source massively parallel data server to manage large-scale analytic data warehouses and business intelligence workloads. It is built and based on PostgreSQL (RDBMS). Greenplum also carries features that are unavailable within PostgreSQL, such as parallel data loading, storage enhancements, resource management, and advanced query optimization.  Greenplum has powerful analytical tools necessary to help you draw additional insights from your data. It is used across many applications, including finance, manufacturing, education, retail, and so on.  Some of the well-known companies using Greenplum are  Walmart, American Express, Asurian, Bank of America, etc.  Besides them, it is even used in professional services, automotive, media, insurance, and retail markets.It is specially designed to manage large-scale data warehouses and business intelligence workloads.  It allows you to spread your data out across a multitude of servers. The architecture is based on an MPP database.  It means it uses several different processing units that work independently using their own resources and dedicated memory—this way, the workload is shared across multiple devices instead of just one. MPP databases scale horizontally by adding more compute resources (nodes).( Image source: DZone)Just like PostgreSQL, Greenplum leverages one master server, or host, which is the entry-point to the database, accepting connections, and SQL queries. Unlike PostgreSQL that uses standby nodes to geographically distribute their deployment, Greenplum uses segment hosts which store and process the data.Advantages of the Greenplum DatabaseHigh Performance: Greenplum has a uniquely designed data pipeline that can efficiently stream data from the disk to the CPU, without relying on the data fitting into RAM. Greenplum’s high performance overcomes the challenge most RDBMS have scaling to petabyte levels of data. It enables you to run analytics directly in the database rather than exporting and running the data in an external analytics engine; this further enhances the performance of the data analysis.Query Optimization: The Greenplum system ensures the fastest responses to all the queries. The Greenplum distributes the load between their different segments and uses all of the system’s resources parallel to process a query. The single query performance has been optimized in Greenplum 6 with the improved OLTP workload capacity. It can query external data sources like Hadoop, ORC, Cloud Storage, Parquet, AVRO, and other Polyglot data stores.Open source: The big advantage of the Greenplum database is that it is an open-source data warehouse project based on PostgreSQL. Since it is open-source, it allows users to get all the advantages that PostgreSQL provides. Greenplum can run on any Linux server, whether it is hosted in the cloud or on-premise, and can run in any environment. Unlike the Oracle database that runs on almost all servers, the Plumb database is limited to Linux servers only. This could be one of the areas where Greenplum has to work in the future.Support for containerization: Greenplum exhibits excellent support for the container model. It can containerize “segments” that are logically isolated workloads and groups of resources. Its support for containerization further facilitates deployment techniques such as champion/challenger or canaries.AI and Machine Learning: The Greenplum v6 adds more machine learning support and clears the way for deep learning. Greenplum's ability to process large volumes of data at high speeds makes it a powerful tool for smart applications that need to interact intelligently based on an unlimited number of unique scenarios.Polymorphic Data Storage:  The polymorphic data storage enables you to control the configuration for your table. It also gives the liberty to partition storage and compress files within it at any time.Integrated in-database analytics: Apache MADlib is an open-source, SQL-based machine learning library that runs in-database on Greenplum. The library extends the SQL capabilities of the Greenplum Database through user-defined functions. Besides that, users can use a range of power analytics tools with Greenplum like R statistical language, SAS, and Predictive Modeling Markup Language (PMML).The Greenplum is undoubtedly a great database, but it is competing against some strong contenders like Amazon Redshift and Impala. The Greenplum usability and prominence would mostly rely on how quickly they introduce the latest technology in their model at lower rates or free. 
RDBMS (Relational Database Management System) is always under the scanner in terms of its efficiency to handle Big Data, especially if it is unstructured data. Since the existence of both Big Data and RDBMS are evident, new technologies are developed for their peaceful co-existence.Greenplum database is one among them. What is the Greenplum Database?Greenplum Database is an open-source massively parallel data server to manage large-scale analytic data warehouses and business intelligence workloads. It is built and based on PostgreSQL (RDBMS). Greenplum also carries features that are unavailable within PostgreSQL, such as parallel data loading, storage enhancements, resource management, and advanced query optimization.  Greenplum has powerful analytical tools necessary to help you draw additional insights from your data. It is used across many applications, including finance, manufacturing, education, retail, and so on.  Some of the well-known companies using Greenplum are  Walmart, American Express, Asurian, Bank of America, etc.  Besides them, it is even used in professional services, automotive, media, insurance, and retail markets.It is specially designed to manage large-scale data warehouses and business intelligence workloads.  It allows you to spread your data out across a multitude of servers. The architecture is based on an MPP database.  It means it uses several different processing units that work independently using their own resources and dedicated memory—this way, the workload is shared across multiple devices instead of just one. MPP databases scale horizontally by adding more compute resources (nodes).( Image source: DZone)Just like PostgreSQL, Greenplum leverages one master server, or host, which is the entry-point to the database, accepting connections, and SQL queries. Unlike PostgreSQL that uses standby nodes to geographically distribute their deployment, Greenplum uses segment hosts which store and process the data.Advantages of the Greenplum DatabaseHigh Performance: Greenplum has a uniquely designed data pipeline that can efficiently stream data from the disk to the CPU, without relying on the data fitting into RAM. Greenplum’s high performance overcomes the challenge most RDBMS have scaling to petabyte levels of data. It enables you to run analytics directly in the database rather than exporting and running the data in an external analytics engine; this further enhances the performance of the data analysis.Query Optimization: The Greenplum system ensures the fastest responses to all the queries. The Greenplum distributes the load between their different segments and uses all of the system’s resources parallel to process a query. The single query performance has been optimized in Greenplum 6 with the improved OLTP workload capacity. It can query external data sources like Hadoop, ORC, Cloud Storage, Parquet, AVRO, and other Polyglot data stores.Open source: The big advantage of the Greenplum database is that it is an open-source data warehouse project based on PostgreSQL. Since it is open-source, it allows users to get all the advantages that PostgreSQL provides. Greenplum can run on any Linux server, whether it is hosted in the cloud or on-premise, and can run in any environment. Unlike the Oracle database that runs on almost all servers, the Plumb database is limited to Linux servers only. This could be one of the areas where Greenplum has to work in the future.Support for containerization: Greenplum exhibits excellent support for the container model. It can containerize “segments” that are logically isolated workloads and groups of resources. Its support for containerization further facilitates deployment techniques such as champion/challenger or canaries.AI and Machine Learning: The Greenplum v6 adds more machine learning support and clears the way for deep learning. Greenplum's ability to process large volumes of data at high speeds makes it a powerful tool for smart applications that need to interact intelligently based on an unlimited number of unique scenarios.Polymorphic Data Storage:  The polymorphic data storage enables you to control the configuration for your table. It also gives the liberty to partition storage and compress files within it at any time.Integrated in-database analytics: Apache MADlib is an open-source, SQL-based machine learning library that runs in-database on Greenplum. The library extends the SQL capabilities of the Greenplum Database through user-defined functions. Besides that, users can use a range of power analytics tools with Greenplum like R statistical language, SAS, and Predictive Modeling Markup Language (PMML).The Greenplum is undoubtedly a great database, but it is competing against some strong contenders like Amazon Redshift and Impala. The Greenplum usability and prominence would mostly rely on how quickly they introduce the latest technology in their model at lower rates or free. 

RDBMS (Relational Database Management System) is always under the scanner in terms of its efficiency to handle Big Data, especially if it is unstructured data. Since the existence of both Big Data and RDBMS are evident, new technologies are developed for their peaceful co-existence.

Greenplum database is one among them. 

What is the Greenplum Database?

Greenplum Database is an open-source massively parallel data server to manage large-scale analytic data warehouses and business intelligence workloads. It is built and based on PostgreSQL (RDBMS). Greenplum also carries features that are unavailable within PostgreSQL, such as parallel data loading, storage enhancements, resource management, and advanced query optimization

 

Greenplum has powerful analytical tools necessary to help you draw additional insights from your data. It is used across many applications, including finance, manufacturing, education, retail, and so on.  Some of the well-known companies using Greenplum are  Walmart, American Express, Asurian, Bank of America, etc.  Besides them, it is even used in professional services, automotive, media, insurance, and retail markets.

It is specially designed to manage large-scale data warehouses and business intelligence workloads.  It allows you to spread your data out across a multitude of servers. 

The architecture is based on an MPP database.  It means it uses several different processing units that work independently using their own resources and dedicated memory—this way, the workload is shared across multiple devices instead of just one. MPP databases scale horizontally by adding more compute resources (nodes).

( Image source: DZone)

Just like PostgreSQL, Greenplum leverages one master server, or host, which is the entry-point to the database, accepting connections, and SQL queries. Unlike PostgreSQL that uses standby nodes to geographically distribute their deployment, Greenplum uses segment hosts which store and process the data.

Advantages of the Greenplum Database

  • High Performance: Greenplum has a uniquely designed data pipeline that can efficiently stream data from the disk to the CPU, without relying on the data fitting into RAM. Greenplum’s high performance overcomes the challenge most RDBMS have scaling to petabyte levels of data. It enables you to run analytics directly in the database rather than exporting and running the data in an external analytics engine; this further enhances the performance of the data analysis.
  • Query Optimization: The Greenplum system ensures the fastest responses to all the queries. The Greenplum distributes the load between their different segments and uses all of the system’s resources parallel to process a query. The single query performance has been optimized in Greenplum 6 with the improved OLTP workload capacity. It can query external data sources like Hadoop, ORC, Cloud Storage, Parquet, AVRO, and other Polyglot data stores.
  • Open source: The big advantage of the Greenplum database is that it is an open-source data warehouse project based on PostgreSQL. Since it is open-source, it allows users to get all the advantages that PostgreSQL provides. Greenplum can run on any Linux server, whether it is hosted in the cloud or on-premise, and can run in any environment. Unlike the Oracle database that runs on almost all servers, the Plumb database is limited to Linux servers only. This could be one of the areas where Greenplum has to work in the future.
  • Support for containerization: Greenplum exhibits excellent support for the container model. It can containerize “segments” that are logically isolated workloads and groups of resources. Its support for containerization further facilitates deployment techniques such as champion/challenger or canaries.
  • AI and Machine Learning: The Greenplum v6 adds more machine learning support and clears the way for deep learning. Greenplum's ability to process large volumes of data at high speeds makes it a powerful tool for smart applications that need to interact intelligently based on an unlimited number of unique scenarios.
  • Polymorphic Data Storage:  The polymorphic data storage enables you to control the configuration for your table. It also gives the liberty to partition storage and compress files within it at any time.
  • Integrated in-database analytics: Apache MADlib is an open-source, SQL-based machine learning library that runs in-database on Greenplum. The library extends the SQL capabilities of the Greenplum Database through user-defined functions. Besides that, users can use a range of power analytics tools with Greenplum like R statistical language, SAS, and Predictive Modeling Markup Language (PMML).

The Greenplum is undoubtedly a great database, but it is competing against some strong contenders like Amazon Redshift and Impala. The Greenplum usability and prominence would mostly rely on how quickly they introduce the latest technology in their model at lower rates or free. 

Before answering the difference between database and data warehouse, let me explain about data analytics. Because if you don't understand the importance of analytics, discussing the difference of a database and a data warehouse is irrelevant.  The future business depends on data, and to use collected data or past data, it must be analyzed and cleaned. And for the same, data analytics is used to get better insights out of that and increase the ability to use the massive amounts of data. As a result, data analytics help to assist accurate data and returns with a quality decision.   A database represents elements of the real world. It is designed to be built and populated with data for a specific task. It is also a building block of your data solution. ACID compliance is followed by a database system ( i.e., Atomicity, Consistency, Isolation, and Durability). Here, are key reasons for using Database:   A database suggests various techniques to stock up and quickly retrieve it.  Database act as a well-organized handler to balance the requirement of multiple applications using the same data.  A DBMS offer limits to get highly secure data to and to prevent access to actionable data.  It offers data safety and its appropriate access.  A database allows you to access simultaneous data for a single user to access the data at a time.   On the other end, information which stores past data and commutative data from various resources is a data warehouse. It is intended to examine, report, incorporate transaction data from different sources. Data Warehouse eases the process of reporting and putting results together with the help of analysis. Here are key reasons for using Data Warehouse:   It helps you to integrate multiple data resources to reduce stress on the production system.  Data warehouse helps you to reduce TAT (total turnaround time) for analysis and reporting.  The data warehouse system provides more accurate reports for the business.  It saves the user's time for data retrieving by accessing critical data from different sources in a single place.  Data warehouse allows you to stores a large amount of historical data to analyze different periods and trends to make future predictions.  It enhances the value of operational business applications and customer relationship management systems.   Data warehouse  Pros: Better support for big data, analysis, reporting, data retrieval, and more. It is specially designed to stock data from various sources.  Cons: It may coast high to business as compared to a single database. And it has less control over access and security configuration.  Database  Pros: Processing digital transactions.  Cons: Reporting, analysis, and visualization may not be able to perform across a large integrated set of data set.  In addition to the above discussion, I can say that the data warehouse helps you to analyze and investigate business insights whereby the database helps to perform the essential business operation. Ultimately, the data-driven business environment in these fast world of social media and data relies on speedy, and thorough analysis. You can choose one of them as per your business requirements.
Before answering the difference between database and data warehouse, let me explain about data analytics. Because if you don't understand the importance of analytics, discussing the difference of a database and a data warehouse is irrelevant.  The future business depends on data, and to use collected data or past data, it must be analyzed and cleaned. And for the same, data analytics is used to get better insights out of that and increase the ability to use the massive amounts of data. As a result, data analytics help to assist accurate data and returns with a quality decision.   A database represents elements of the real world. It is designed to be built and populated with data for a specific task. It is also a building block of your data solution. ACID compliance is followed by a database system ( i.e., Atomicity, Consistency, Isolation, and Durability). Here, are key reasons for using Database:   A database suggests various techniques to stock up and quickly retrieve it.  Database act as a well-organized handler to balance the requirement of multiple applications using the same data.  A DBMS offer limits to get highly secure data to and to prevent access to actionable data.  It offers data safety and its appropriate access.  A database allows you to access simultaneous data for a single user to access the data at a time.   On the other end, information which stores past data and commutative data from various resources is a data warehouse. It is intended to examine, report, incorporate transaction data from different sources. Data Warehouse eases the process of reporting and putting results together with the help of analysis. Here are key reasons for using Data Warehouse:   It helps you to integrate multiple data resources to reduce stress on the production system.  Data warehouse helps you to reduce TAT (total turnaround time) for analysis and reporting.  The data warehouse system provides more accurate reports for the business.  It saves the user's time for data retrieving by accessing critical data from different sources in a single place.  Data warehouse allows you to stores a large amount of historical data to analyze different periods and trends to make future predictions.  It enhances the value of operational business applications and customer relationship management systems.   Data warehouse  Pros: Better support for big data, analysis, reporting, data retrieval, and more. It is specially designed to stock data from various sources.  Cons: It may coast high to business as compared to a single database. And it has less control over access and security configuration.  Database  Pros: Processing digital transactions.  Cons: Reporting, analysis, and visualization may not be able to perform across a large integrated set of data set.  In addition to the above discussion, I can say that the data warehouse helps you to analyze and investigate business insights whereby the database helps to perform the essential business operation. Ultimately, the data-driven business environment in these fast world of social media and data relies on speedy, and thorough analysis. You can choose one of them as per your business requirements.

Before answering the difference between database and data warehouse, let me explain about data analytics. Because if you don't understand the importance of analytics, discussing the difference of a database and a data warehouse is irrelevant. 

The future business depends on data, and to use collected data or past data, it must be analyzed and cleaned. And for the same, data analytics is used to get better insights out of that and increase the ability to use the massive amounts of data. As a result, data analytics help to assist accurate data and returns with a quality decision.  

A database represents elements of the real world. It is designed to be built and populated with data for a specific task. It is also a building block of your data solution. ACID compliance is followed by a database system ( i.e., Atomicity, Consistency, Isolation, and Durability). Here, are key reasons for using Database:  

  • A database suggests various techniques to stock up and quickly retrieve it. 
  • Database act as a well-organized handler to balance the requirement of multiple applications using the same data. 
  • A DBMS offer limits to get highly secure data to and to prevent access to actionable data. 
  • It offers data safety and its appropriate access. 
  • A database allows you to access simultaneous data for a single user to access the data at a time.  

On the other end, information which stores past data and commutative data from various resources is a data warehouse. It is intended to examine, report, incorporate transaction data from different sources. Data Warehouse eases the process of reporting and putting results together with the help of analysis. Here are key reasons for using Data Warehouse:  

  • It helps you to integrate multiple data resources to reduce stress on the production system. 
  • Data warehouse helps you to reduce TAT (total turnaround time) for analysis and reporting. 
  • The data warehouse system provides more accurate reports for the business. 
  • It saves the user's time for data retrieving by accessing critical data from different sources in a single place. 
  • Data warehouse allows you to stores a large amount of historical data to analyze different periods and trends to make future predictions. 
  • It enhances the value of operational business applications and customer relationship management systems.  

Data warehouse 

Pros: Better support for big data, analysis, reporting, data retrieval, and more. It is specially designed to stock data from various sources. 

Cons: It may coast high to business as compared to a single database. And it has less control over access and security configuration. 

Database 

Pros: Processing digital transactions. 

Cons: Reporting, analysis, and visualization may not be able to perform across a large integrated set of data set. 

In addition to the above discussion, I can say that the data warehouse helps you to analyze and investigate business insights whereby the database helps to perform the essential business operation. Ultimately, the data-driven business environment in these fast world of social media and data relies on speedy, and thorough analysis. You can choose one of them as per your business requirements.

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