What are the Different Categories of Databases?
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.
Personal Database – 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.
Centralized Database – 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.
Distributed Database – 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.
Operational Database – 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.
Relational Database – 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.
NoSQL Database – 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.
Cloud Database – 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.
Overview of Different Databases
Guide to MySQL
MySQL database 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 database with 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. Read more about RAID Storage for Databases in this blog.
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.
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.
- 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
Guide to 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.
Apache Cassandra Architecture
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
- Cassandra doesn’t support aggregations and joins
- Cassandra isn’t a Data Warehouse
Introduction to Azure SQL
PaaS is the category in which the Azure SQL database 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.
Azure SQL Benefits
- 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
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
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
Apache Druid Benefits
- 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
Overview of Snowflake Computing
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.
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 Relational Approach to Database Management
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 –
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