The correct schema is crucial in supporting modern data and analytics. Database schemas can be challenging, but we look at how you can design the correct schema for your data stores – starting right from the definition of a “database schema” itself.
What is Database schema?
A database schema is a structure that defines the logical view of the entire database Schema defines how the data is managed and how the relations among them are associated. It formulates all the constraints that are to be applied to the data.
Now let us dive right into the different categories of Database.
What are the Different Categories of Databases?
The below highlighted are the various categories of Databases
End User Database
End User Database
The use of this kind of database is relative to end-users who consider software or application only as their work environment. End-User Databases mostly use shared databases to complete the demand of end-users only. The primary goal is to set up and complete all the requirements of an end-user.
When data needs reside for small management or a group and data is preserved in the personal computer only. Personal Databases mostly used in short term project goals.
Remote access to data, data at distinct locations and Database at one location, all three makes a database centralized. Users from all locations had access to this centralized database that can be accessed at any time. A local area handler is the best example of this centralized database where procedures are followed to complete design flow.
This database is opposite in the implementation of centralized databases. The data in these databases is not centralized to one location (physical) but at different physical locations. All these locations are connected via some procedural communication links. They are designed to store and retrieve data faster.
Bussiness centric operations and flow are based on operational databases such as Customer Relationship Management and Enterprise resource planning software. CRMs and ERPs use functional kinds of databases.
When data needed to fit into the predefined category of tables where schema, storage types and data types are present, and data is structured, relational databases are used as they are easy to extend, join and many standard and straightforward operations are easy to apply on those tables.
Relational databases were not useful in solving big data-related issues but NoSQL databases resolved those issues, and moreover data from different distributed locations of cloud can also be accessed within NoSQL and Data doesn’t need to be structured only.
When Scalability, storage cost, and bandwidth are essential, cloud databases come with the super solution of this. Cloud databases are kind of a virtual environment where data of all types can be stored, and moreover, big data operations are efficient to perform on these databases. The logic behind these is Software as a service to Database as a service.
RAID storage uses different disks to provide fault tolerance, to improve overall performance, and to increase storage size in a system. Click to explore about, Types of RAID Storage for Databases
What are the various types of Databases?
There are various types of Databases
MySQL is best suitable for almost any data storage needs. It helps to scale database for cases like management applications where data is originated in a particular manner or structure as defined for implying organizational needs and structure. It can easily share the data and can join the data from different tables to generate some data knowledge or pattern. It is an open-source and has the largest community so almost every issue can quickly be resolved. Many companies are relying on MySQL for their use cases such as from Twitter(using it to manage real-time tweet and retweet counts) to small management enterprises.
What are the benefits of Using MySQL?
MySQL Enterprise can help to monitor real-time availability.
It can integrate with DevOps and Cloud environment also.
SQL, NoSQL can be combined through MySQL.
Joins support help to scale data for multiple use cases quickly and fact tables can also be used to obtain fact-specific information.
MySQL has an issue of high and extensive connection churn as most of its resources are used in concurrent request sessions. Real-time logging troubleshooting is slow or not available as it costs higher and is disabled by default. Development time is high as compared to other databases as changes (if made) need extensive expertise to optimize master/slave or multi-master architecture.
What is MongoDB?
MongoDB use cases involve around faster search operations, documented storage, and real-time metadata management. Companies like UIDAI and eBay are using MongoDB for their Use purposes. UIDAI is using MongoDB to store and search images faster, and so does the Shutterfly. Shutterfly also uses this for metadata management after implementing various databases like Oracle and Cassandra, and they Quoted MongoDB as best fit but without compromise.
What are the Benefits MongoDB?
The storage format is vital to value pairs hence searching is faster and also has in place update capability.
Heterogeneous data can be managed, and sharding can be implemented at any scale.
Powerful SQL query structure is used to enhance performance and data can be easily distribute to other locations.
There is no stored procedure compatibility in MongoDB, so the logic binding is difficult as well as joins are also not supported Comparatively more complex structure for transactions and NoSQL makes it difficult to support ACID properties.
Amazon Redshift Architecture
Running Data warehouse is not a well-thinked case but running it for complex and some mission-critical use case is the thing. Redshift provides a use case for mission-critical workloads as well as for extensive transactional logging. Redshift performs traditional data warehousing in a very smooth manner with the support of always-available services. For example, NASDAQ reporting system is based on Redshift so any critical data load mistake can put oneself to jail also. Amazon Redshift Benefits
Automatic administrative tasks, SQL like query structure and easy to use UI makes it more adaptable
It is very cost effective and more AWS components can be integrated easily
It has integration support with JDBC like drivers that help to access SQL databases for specific use cases
Problems with Amazon Redshift
The sensitivity of data such as Private data is not well defined as it is a cloud-based solution and sensitive data must not be stored on the cloud
There is no inbuilt capability in redshift for data uniqueness, and it is needed to implement on application end or functional side
Parallel uploading is only supported for services such as S3, DynamoDB, and EMR
What is BigQuery?
BigQuery provides best out of the use cases like massive, fast SQL querying, massive data sets and a single view of data points. Moreover, its use cases rely on secure Access, and BigQuery architecture is considered as a use case of Dremel technology that provides the fastest and best results once the query is executed. Data warehouse as a service is not the only case with BigQuery but collaborating with other datasets at a massive scale and a single view for multiple data viewpoints.
Key Benefits of using BigQuery
The structure of datasets, tables, rows, and columns helps to adapt BigQuery quickly
Multi-level execution tree on thousands of servers can process data faster and join collectively at root
Problems with BigQuery
It allows only one Join per query, so need to use the nested structure of questions for getting the work is done The documentation says to use a TOP function instead of GROUP BY on multiple groups but TOP also produces one group Getting data from files is very difficult as if there exists an error in the data we need to solve it locally and re-upload those files.
When there is a need to customize and load data on free peer-to-peer connection and scalability is required to expand by expanding nodes (not hardware), Cassandra is the best fit. Also, when there is more write request than to reading Cassandra is best suitable as it uses nodes architecture to write at many distributed server nodes. This is the first of its kind database that uses a distributed node structure. Data partitioning is also supported, and data can be accessed by a defined unique primary key. IoT data can be easily maintained with Cassandra and Time series data also as Facebook designs it for this use case. Apache Cassandra Benefits
Always on architecture for the continuous availability of data resource
Natively Distributed for replication and processing of a large amount of data over several nodes and distributed servers
Fast linear scale performance
It has multiple secondary indexes for each table
The data model is flexible as it allows you to add entities or attributes over time
Problems with Apache Cassandra
Updates and deletes are individual implementation cases of Write but not immediate, also read operations are comparatively slower than writes
PaaS is the category in which the Azure SQL stands. Pay as you go when more scalability is required on SQL database with no interruptions. It can be used as a single database, elastic pool or as a managed instance. Capable of creating Virtual machines with SQL server. Grisard management AG is using Azure SQL platform that trims their cost to 40% as they described it as a cost-effective and fast architecture to work on. WhiteSource is also using the Azure platform and Azure Kubernetes service for streamline application development.
What are the benefits of Azure SQL?
It implements a fully managed database service, and SQL server is never needed to manage and update
It has approximate query performance capability that makes it somewhat intelligent by default
It is not very costly and provides more managed services on data warehouse and database storages
Problems with Azure SQL
It is not adequate to use the Azure platform for small datasets as it cost higher to manage such data sets
Some SQL server functionalities are not available in Azure SQL database and migration
From on-premise to Azure need some changes to made before migrating. But is it easier to that.
Oracle Database Architecture
When there is a need to put the development and testing of data on the cloud, Oracle has the best use case, or we can say it is best to use Oracle database for such cases. Every update of Oracle databases contains new technology updates too, but data will not affect new technologies. It will remain as previous. Use case of Oracle database on cloud increased every year as they provide more in- Memory capabilities to look into problem and technology advancements are also making transactions faster.
Benefits of using Oracle Database
Oracle databases have more customer satisfaction as compared to other databases as every oracle database is backward compatible
They are more functional as they are used in almost every corporate use case is handled by oracle databases.
Fully managed ACID support is available, makes business use cases more efficient.
Guide to IBM DB2
IBM brings faster and scalable database DB2 that always fulfills the requirements of every use case. It has the inbuilt capability of intelligence that quickly adopts the elements and works according to them. IBM Watson analytics is built over core DB2 and Netezza engines. Watson is the biggest analytics tool in the market and supposed to solve every use case as Netezza engines are used with it that increases the performance of querying data.
Benefits of using IBM DB2
IBM DB2 has flexible platform support
It can create large virtual pool buffer that may help to expand the business dataset sizes
DB2 is cheaper than Oracle products, so might play as a cost-effective player
Problems with DB2
Uses 31 bit addressing whereas competitor products have 64 bit addressing
There are multiple tools available that is excellent, but most of the times it is confusing to choose as many tools can obtain help resolve the same business logic
Snowflake is ready to use a solution that the user requires to just use it directly without worrying about its installation and deployment and then its startup. Click to explore about, Snowflake Cloud Data Warehouse Architecture
Apache Druid Architecture
The use case of Druid defines that better performance analytics can be performed by using Druid as a Database service or warehouse service. It works better with Kafka streamline topics as it is efficient to load data from Kafka topics. Stream data, time-series data or click events data can be optimized, used for Business Analytics and Business Intelligence operations. Some use cases define that Druid is capable of troubleshooting the root of the problem caused. Digital marketing, Network Flows, and IoT & Device management are some of the best and suitable use cases for Druid application development.
What are the benefits of Apache Druid?
Queries can auto manage Sub-Second OLAP
Druid Offers lock-free data ingestion for streaming sources like Kafka
Fast, as in, It can process thousands of queries per second
Best aggregation performance throughput for Business Intelligence and analytics
Problems with Apache Druid
Choose one card of Druid is not correctly choose one in 99% cases as described by various usages by various companies
Aggregated data is stored, no row-level analytics can be performed
Better to be used only if the primary goal is Time series data
Migration and conversion are significant factors that tend to use Snowflake. Companies like CapSecurity describes that they increase reporting speed up to 200 times from Snowflake as compared to their previous use case. Snowflake encrypts data by default, and semi-structured data can also be processed with SQL in a structured way. This use case increases the capability of where snowflake fits in.
What are the benefits of Snowflake?
Data without being encrypted isn’t allowed
It can load semi-structured data quickly without even defining schema by end users
Users can query semi-structured data just like structured data in SQL way and also joins possible to apply (but need to implement my own)
Can handle an unlimited number of simultaneous users.
It is not OLTP replacement but can handle OLTP data effectively as compared to legacy.
A fully managed database service helps to set up, manage, and administer your database in the cloud and manage and also provide services for hardware provisioning and Backup. To optimize your database for Memory and performance, we recommend taking the following steps -