Computer Science
NoSQL Databases
NoSQL databases are a type of database management system that provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. NoSQL databases are designed to handle large volumes of data and are often used in big data and real-time web applications due to their flexibility and scalability.
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12 Key excerpts on "NoSQL Databases"
eBook - PDFGeographical Information Systems
Trends and Technologies
- Elaheh Pourabbas(Author)
- 2014(Publication Date)
- CRC Press(Publisher)
The following sections focus on different NoSQL spatial systems: CouchDB, MongoDB, BigTable, and Neo4j. Finally, we conclude the chapter and compare the several systems addressed herein. NoSQL Systems NoSQL is a term in information technology that describes database management systems, which depart from classic Relational DBMS altogether, or in some parts. The term was first used in 1998 for lightweight open source databases that did not use SQL (Structured Query Language) as a database language (Strozzi 2007). The name “NoSQL” could indicate that these databases do not support SQL, but in this case it actually means “Not Only SQL” (NoSQL 2013). During the past years, the term has been widely used to designate a class of database systems that offer an alternative solution to the Relational Model (Codd 1970) in terms of availability, scalability, and performance for the management of large amounts of data. The goal of NoSQL systems is not to replace RDBMs as a whole, but to be used in cases in which more flexibility is required with respect to the database structure (Sadalage and Fowler 2012; Redmond and Wilson 2012). A Brief History Google’s BigTable (Chang et al. 2008) and Amazon’s Dynamo (DeCandia et al. 2007) seem to be the starting point for the NoSQL movement. Many of the design decisions and principles used in these systems can be found in later ones. Google’s BigTable was launched in 2004 as a high-performance proprietary database offering scalability and availability. Its original proposal was to relax the structure used by the Relational Model. Amazon’s Dynamo was launched in 2007 as a high-performance non-relational database system, to be used by Amazon Web Services. In 2008, Cassandra was presented as a highly scalable distributed database system, designed by Facebook developers to deal with large amounts of data (Lakshman and Malik 2009). Cassandra joined the distributed systems technologies from Dynamo and the data model from Google’s BigTable.
eBook - ePubBig Data
Concepts, Technology, and Architecture
- Balamurugan Balusamy, Nandhini Abirami R, Seifedine Kadry, Amir H. Gandomi(Authors)
- 2021(Publication Date)
- Wiley(Publisher)
Schemaless databases are those that do not require any rigid schema to store the data. They can store data in any format, be it structured or unstructured.- What is a NoSQL Database?
A NoSQL, or Not Only SQL, database is a non‐relational database designed to store and retrieve semi‐structured and unstructured data. It was designed to overcome big data’s scalability and performance issues, which traditional databases were not designed to address. It is specifically used when organizations need to access, process, and analyze a large volume of unstructured data.- What is the difference between NoSQL and a traditional database?
RDBMS is a schema‐based database system as it first creates a relation or table structure of the given data to store them in rows and columns and uses primary key and foreign key. It takes a significant amount of time to define a schema, but the response time to the query is faster. The schema can be changed later, but this requires a significant amount of time.Unlike RDBMS, NoSQL Databases don’t have a stringent requirement for the schema. They have the capability to store the data in HDFS as it arrives and later a schema can be defined using Hive to query the data from the database.- What are the features of NoSQL database?
- Schemaless
- Horizontal scalability
- Distributed computing
- Low cost
- Non‐relational
- Handles large volume of data
- What are the types of NoSQL Databases? The four types of NoSQL Databases are:
- Key‐value store database
- Column‐store database
- Document database
- Graph database
- What is a key‐value store database?
A key‐value store database is the simplest and most efficient database that can be implemented easily. It allows the user to store data in key‐value pairs without any schema. The data is usually split into two parts: key and value. The key is a string, and the value is the actual data; hence the reference key‐value pair.- What is a graph‐oriented database?
A graph‐oriented database stores the entities also known as nodes and the relationships between them. Each node has properties and the relationships between the nodes are known as edges. The relationships have properties and directional significance. The properties of the relationships are used to query the graph database.
eBook - ePubBig Data
Concepts, Warehousing, and Analytics
- Maribel Yasmina Santos, Carlos Costa(Authors)
- 2022(Publication Date)
- River Publishers(Publisher)
3 OLTP-ORIENTED DATABASES FOR BIG DATA ENVIRONMENTSINTRODUCTION
Over the last years, the NoSQL movement has revolutionized the way we think about databases that support large-scale enterprise applications. NoSQL Databases can be classified according to different types and can also have various data models, being frequently recognized as schema-less storage systems capable of serving low latency applications that handle huge amounts of data. This chapter presents an overview of the NoSQL movement and the various types of NoSQL Databases, including practical examples for Redis (key-value store), HBase (wide column store), MongoDB (document store), and Neo4j (graph database). Moreover, the chapter concludes with some insights regarding NewSQL databases, a relatively recent movement focused on bridging the gap between SQL and NoSQL Databases by trying to combine the advantages of both.3.1. NOSQL AND NEWSQL: AN OVERVIEW
Database technology has evolved significantly to handle datasets at different scales and support applications that may have high needs for random access to data (M. Chen et al., 2014; Hashem et al., 2015). NoSQL Databases became popular mainly due to the lack of scalability in RDBMSs, since this new type of databases provides mechanisms to store and retrieve large volumes of distributed data (Hashem et al., 2015). The relevant factors that motivated the appearance of NoSQL Databases were the strictness of the existing relational model and, consequently, its inadequacy for storing Big Data. NoSQL Databases are regarded as distributed, scalable, elastic, and fault-tolerant storage systems. They satisfy an application’s need for high availability, even when nodes fail, appropriately replicating data across multiple machines (Kambatla et al., 2014). Relational databases will certainly evolve and some organizations, such as Facebook, are using mixed database architectures (M. Chen et al., 2014). Combining the benefits of both storage systems is a current research trend, as was previously mentioned (Cuzzocrea et al., 2011). A recent term is emerging, “NewSQL”, which combines the relational data model with certain benefits of NoSQL systems, such as scalability, for example (Grolinger, Higashino, Tiwari, & Capretz, 2013). According to Cattell (2011), NoSQL and NewSQL databases are mainly designed to scale OLTP-style workloads over several nodes, hence fulfilling the requirements of environments with millions of simple operations (e.g., key lookups and reads/writes of one record or a small number of records).
eBook - ePubBig Data and Hadoo - 2nd Edition
Fundamentals, tools, and techniques for data-driven success (English Edition)
- Mayank Bhushan(Author)
- 2023(Publication Date)
- BPB Publications(Publisher)
HAPTER 2NoSQL Data Management IntroductionNowadays, Relational Database Management Systems (RDBMS ) is the predominant technology for storing structured data in Web and business applications. The publication of Codd’s paper titled A Relational Model of Data for Large Shared Data Banks in 1970, these data repositories, which rely on the relational calculus and offer extensive ad hoc querying capabilities through SQL, have been widely embraced and are commonly regarded as the exclusive option for storing data that can be accessed consistently by multiple clients.Although there are totally different approaches over the years, like object databases or XML stores, these technologies have not gained a similar adoption and market share as RDBMSs. Rather, these alternatives have either been absorbed by RDBMS that enables storing XML and use it for functions like text classification, or they need to become niche products, for example, OLAP or stream processing.NoSQL was used for the first time in 1998 to suggest switching from traditional system RDBMS to NoSQL. This again raised its usability in 2009 with strong recommendations for that system to change from the existing one. Last.fm developer Jon Oskarsson organized a meeting to show its usage and popularity in San Francisco in which most technocrats agreed to adopt the changes.In contrast to RDBMS systems, most NoSQL Databases are designed to scale well in the horizontal direction and do not believe exclusive hardware is purposely used. Machines are often additional and removed (or crash) without inflicting equivalent operational efforts to perform sharding in RDBMS cluster-solutions; some NoSQL datastores even give automatic sharding. Javier Soltero, CTO of SpringSource, puts it this way: Oracle would tell you that with the correct degree of hardware and also the right configuration of Oracle RAC (Real Application Clusters) and alternative associated magic code , Last.fm states: Web two.0 corporations will take a chance and that they would like quantifiability. Once you have these two things together, it makes [NoSQL] terribly compelling . Blogger Nati Shalom agrees with that:cost pressure conjointly forced several organizations to look at less expensive alternatives, and therewith came analysis that showed that distributed storage-supported goods hardware is even additional reliable than several high-end databases
eBook - ePubBig Data and Hadoop- Learn by Example
Learn by example
- Mayank Bhushan(Author)
- 2018(Publication Date)
- BPB Publications(Publisher)
HAPTER 2NoSQL Data Management
2.1 Introduction to NoSQL database
Relational database management systems (RDBMS) these days are predominant technology used for storing structured data in web and business applications. Since Codds paper “A relative model of information for large shared data banks” from 1970 these data stores rely on the relational calculus and provides comprehensive ad hoc querying facilities by SQL, and these are widely adopted, are usually thought of because of solely different for data storage accessible by multiple clients in a consistent manner.Although there are totally different approaches over the years like object databases or XML stores these technologies haven't gained a similar adoption and market share as RDBMSs. Rather, these alternatives have either been absorbed by RDBMS and enable to store XML and use it for functions like text classification or they need become niche product for e.g. OLAP or stream processing.Very first use of NoSQL was in 1998 for suggesting to switch from traditional system RDBMS to NoSQL. This was again raising its usability in 2009 with strong recommendation of that system to change from existing one. Last fm developer Jon Oskarsson organized a meeting to show about its usages and popularity in San Francisco in which most of the technocrats agree to adopt the changes.In contrast to RDBMS systems most NoSQL Databases are designed to scale well in the horizontal direction, not only for hardware purpose. Machines are often additional and removed without inflicting equivalent operational efforts to perform sharding in RDBMS cluster-solutions; some NoSQL datastores even give automatic sharding. According to Javier Soltero, CTO of Spring Source: “Oracle would tell you the correct degree of hardware and also the right configuration of Oracle RAC (Real Application Clusters) and alternative associated magic code, Last.fm states: “Web two.0 corporations will take a chance and that would like quantifiability. Once you have these two things together, it makes [NoSQL] terribly compelling.” Blogger Nati Shalom says: “cost pressure conjointly forced several organizations to be less expensive alternatives, and came with analysis that showed that distributed storage which is supported by goods hardware and is even additional reliable then several high end database.
eBook - PDFBig Data
Storage, Sharing, and Security
- Fei Hu(Author)
- 2016(Publication Date)
- Auerbach Publications(Publisher)
These databases are capable of processing the large heterogeneous data collected from the Internet and other sensor platforms. One style of NoSQL Databases that have become used for applications that require support for high velocity data ingest and relatively simple cell-level queries are key-value stores. As a result, the majority of the volume of data on the Internet is now analyzed using key-value stores such as Amazon Dynamo [23], Cassandra [44], and HBase [32]. Key-value stores and other NoSQL Databases compromise on data consistency in order to provide higher performance. In response to this challenge, the relational database community has developed a new class of relational databases (often referred to as NewSQL) such as SciDB [16], H-Store [37], and VoltDB [64] to provide the features of relational databases while also scaling to very large datasets. Very often, these newSQL databases make use of a differ-ent datamodel [16] or advances in hardware architectures. For example, MemSQL [56] is a distributed in-memory database that provides high-performance, atomicity, consistency, iso-lation, and durability (ACID)-compliant relational database management. Another example, BlueDBM [36], provides high-performance data access through flash storage and field pro-grammable gate arrays (FPGA). In this section, we provide an overview of database management systems, the different generations of databases, and a deep dive into two newer technologies: a key-value store— Apache Accumulo and an array database—SciDB. 2.5.1 Database management systems and features A database is a collection of data and all of the supporting data structures. The software interface between users and a database is known as the database management system. Database management systems provide the most visible view into a dataset. There are many popular database management systems such as MySQL [4], PostgreSQL [63], and Oracle [5].
eBook - PDFLarge Scale and Big Data
Processing and Management
- Sherif Sakr, Mohamed Gaber, Sherif Sakr, Mohamed Gaber(Authors)
- 2014(Publication Date)
- Auerbach Publications(Publisher)
287 9 An Overview of the NoSQL World Liang Zhao, Sherif Sakr, and Anna Liu Over the past decade, rapidly growing Internet-based services such as e-mail, blog-ging, social networking, search, and e-commerce have substantially redefined the way consumers communicate, access contents, share information and purchase prod-ucts. Relational database management systems (RDBMS) have been considered as the one-size-fits-all solution for data persistence and retrieval for decades. However, the ever-increasing need for scalability and new application requirements have cre-ated new challenges for traditional RDBMS. Recently, a new generation of low-cost, high-performance database software, aptly named as NoSQL (Not Only SQL), has emerged to challenge the dominance of RDBMS. The main features of these systems include ability to horizontally scale, supporting weaker consistency models, using flex-ible schemas and data models and supporting simple low-level query interfaces. In this chapter, we explore the recent advancements and the new approaches of web-scale data management. We discuss the advantages and disadvantages of several recently introduced approaches and its suitability to support certain class of applications and CONTENTS 9.1 Introduction .................................................................................................. 288 9.2 NoSQL Key Systems .................................................................................... 291 9.2.1 Google: Bigtable ............................................................................... 291 9.2.2 Yahoo: PNUTS ................................................................................. 293 9.2.3 Amazon: Dynamo ............................................................................. 294 9.3 NoSQL Open Source Projects ...................................................................... 296 9.4 Database-as-a-Service ..................................................................................
eBook - ePubSmart Data
State-of-the-Art Perspectives in Computing and Applications
- Kuan-Ching Li, Beniamino Di Martino, Laurence T. Yang, Qingchen Zhang, Kuan-Ching Li, Beniamino Di Martino, Laurence T. Yang, Qingchen Zhang(Authors)
- 2019(Publication Date)
- Chapman and Hall/CRC(Publisher)
However, they all have query languages with a similar purpose. Most NoSQL Databases include among its features the possibility of running in distributed environments, thus responding to the needs of distributed processing. It is a success factor since it allows to process large amounts of data in a cheaper and more efficient way by running the databases in distributed solutions of cluster-type machines that allow to scale the systems by adding new machines to the cluster. This feature influences the underlying data models of how to manage data consistency or concurrency. Not all NoSQL Databases comply with this characteristic, as is the case of graph-oriented databases that are not designed for execution in a distributed environment. They do not use pre-defined information structuring schemes [ 10 ]. One of the characteristics of the NoSQL Databases that represents a major change with respect to the relational databases is their lack of need to define a schema or the structure of the information that will be stored in it. The origin of this characteristic is found in the conditions of information processing that occur in the Big Data field. In this context, the type of data generated can be semi-structured or unstructured; it is necessary to aggregate together data that are heterogeneous; in many cases, the data that needs to be stored may have errors or there may be empty information fields; the data is generated at a great speed and in huge quantities. Therefore, no schemas are used in the NoSQL Databases, the information to be stored is simply added, and there is no verification process about the structure and types of data used. The only requirement is that the data be compatible with the data model underlying the type of NoSQL database being used
No longer available |Learn moreBig Data Architect's Handbook
A guide to building proficiency in tools and systems used by leading big data experts
- Syed Muhammad Fahad Akhtar(Author)
- 2018(Publication Date)
- Packt Publishing(Publisher)
Not Only SQL . It means that it doesn't rely only on the SQL programming language for manipulating and storing data, but it can be used in conjunction with other programming languages. Now, we will discuss some of the benefits of NoSQL Databases.
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Benefits of NoSQL Databases
NoSQL helps us to deal with data that we were not able to store or maintain using traditional system approaches. The following are the key benefits of NoSQL Databases:- NoSQL provides schema less data storage is one of the main advantages. It will allow the storing of all types of data in different formats and in different schemas, thereby providing more robust and agile development.
- NoSQL servers scales horizontally , which means it is very easy to scale the capacity up or down. Simply add new servers or remove servers to increase or decrease its capacity, storage, and computation power.
- NoSQL works in a clustered environment that is mainly built on commodity hardware, which is much less expensive than a highly reliable server without affecting the performance or reliability.
- NoSQL Databases spread across multiple nodes with replication to multiple servers. Some of NoSQL database frameworks even work without the master slave concept, which makes them highly available with no single point of failure.
These are some of the key advantages of using NoSQL Databases over the traditional approach. Now, moving forward, we will compare NoSQL with RDBMS databases to give you a clear understanding of the differences between both database types. Passage contains an image
NoSQL versus RDBMS
We will now discuss the different characteristics of NoSQL and relational databases with a point by point comparison to give you a clear understanding:RDBMS NoSQL RDBMS are relational databases. NoSQL are normally non-relational databases or distributed databases. RDBMS databases store data in tabular form, which mean it contains data rows and columns. NoSQL Databases are of key-value, document, column based, or graph based datastores. RDBMS has predefined schema. NoSQL have dynamic schema. RDBMS is vertically scalable. It means you can only increase the hardware on the same server, increasing the computation or other hardware resources. 
No longer available |Learn moreGoogle Cloud Platform for Architects
Design and manage powerful cloud solutions
- Vitthal Srinivasan, Janani Ravi, Judy Raj(Authors)
- 2018(Publication Date)
- Packt Publishing(Publisher)
NoSQL Databases
In the previous chapter, we took a closer look at the RDBMS services of GCP, Cloud SQL, and Cloud Spanner. These are great for many use cases, but there are also several situations in which they are not quite the right tool. The NoSQL offerings on the GCP, Bigtable, and Datastore might come in handy here. Bigtable is similar in many ways to Apache's HBase, while Datastore is a document database that competes with alternatives such as MongoDB.Now, one little bit of fine print: in this chapter, we will use the terms NoSQL and RDBMS as if they are perfect alternatives; that is, it might seem like any storage solution that is not an RDBMS is a NoSQL database. That's not quite strictly true. BigQuery, for instance, is a SQL-compliant data warehouse, which is certainly not an RDBMS. So, the term NoSQL really only means that the data is not accessed via SQL; the alternative could be either:- Product-specific syntax (such as the scan syntax in HBase)
- Programmatic access (from a programming language such as Java or Python)
Relational databases use tables, columns, rows, or schemas to store and retrieve data. NoSQL Databases do not use these structures since they opt for more flexible data models, such as documents or rows of key/value pairs (for example graph stores which store social connections as key-value pairs) . Popular expansions of NoSQL include not SQL or not only SQL . Relational databases have important limitations that make them unsuitable for semi-structured data. Common types of semi-structured data include user and session data; chat, messaging, and log data; time series data such as IoT and device data; and large objects such as video and images.Let's start with an understanding of the internal data representation in a couple of important types of NoSQL Databases—but first, a small digression! This digression above is meant to help us remember all that we really need to know about Datastore. Here is the same text, now annotated to make it relevant to NoSQL Databases!
eBook - PDFData Mining and Data Warehousing
Principles and Practical Techniques
- Parteek Bhatia(Author)
- 2019(Publication Date)
- Cambridge University Press(Publisher)
On the other hand, in NoSQL, the data may be stored in aggregate form keeping software development as simple as possible. Secondly, RDBMS requires a rigid schema where NoSQL is schema-less. Relational technology requires strict definition of a schema prior to storing any data into a database. Changing the schema once data is inserted is extremely disruptive and to be completely avoided – the exact opposite of the behavior desired in the Big Data era, where application developers need to constantly – and rapidly – incorporate new types of data to enrich their applications. In comparison, NoSQL allows changing the schema or the format of the data being inserted any time, without application disruption. NoSQL also may not guarantee full ACID (atomicity, consistency, isolation, durability) property but still has a distributed and fault tolerant architecture. Remind Me The reasons for the success of relational databases were their simplicity, power of SQL, support for transaction management, concurrency control, and recovery management. The biggest frustration with the relational database is of impedance mismatch. The difference between the relational model and the in-memory data structures is known as impedance mismatch. During the Internet boom, to store huge data clusters appeared as a possible solution. But, the mismatch between relational databases and clusters led some organization to consider an alternative route to data storage. A NoSQL database provides a mechanism for storage and retrieval of data based on a bunch of data models other than the relational model. Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. The data model (e.g., Key-value data model, Document data model, Column-family model and Graph data model) differs from the RDBMS, and therefore some operations are faster in NoSQL and some are faster in RDBMS.
eBook - PDFPrinciples of Database Management
The Practical Guide to Storing, Managing and Analyzing Big and Small Data
- Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens(Authors)
- 2018(Publication Date)
- Cambridge University Press(Publisher)
specialized types of data or structures. Two of these, XML and object-oriented databases, were discussed earlier. Other types include: • Database systems to deal with time series and streaming events, such as Event Store and Axibase. Such systems represent data as a series of immut- able events over time, making it easier to support use cases such as event monitoring, complex event processing, or real-time analytics. Typically, availability and performance are of high concern for such systems. • Database systems to store and query geospatial data, supporting geospatial operators following the DE-9IM model, which defines relations between polygons as them being equal, touching, disjoint, contained, covered, or intersecting. For example, you can express a “within radius” query as follows: SELECT name, type, location, ST_Distance_Sphere(Point(-70, 40), location) AS distance_in_meters FROM restaurants WHERE type = "french cuisine" ORDER BY distance_in_meters LIMIT 10 • Database systems such as BayesDB, which lets users query the probable implication of their data (for example, to derive which fields in a table are the main predictors to estimate a certain outcome) and simulate what-if scenarios using a Bayesian query language, such as: SIMULATE gdp -- simulate gross domestic product FROM countries -- using table with information on countries -- given the following: GIVEN population_million = 1000, continent = 'asia' LIMIT 10; -- run 10 simulations Summary This chapter has discussed NoSQL Databases, a group of database management systems that have become quite popular throughout the past decade, and represents a shift in thinking toward schema-less structures, horizontal scalability, and non-relational data models and querying facilities. We note, however, that the explosion of popularity of NoSQL data storage layers should be put into perspective, considering their limitations.
Index pages curate the most relevant extracts from our library of academic textbooks. They’ve been created using an in-house natural language model (NLM), each adding context and meaning to key research topics.
