Modern data platforms provide scale and the ability to process big data, which is now a common buzzword used by a wide variety of organizations. In general, big data refers to the vast amounts of information that organizations create and collect on a daily basis. With the emergence of Redshift and Snowflake, data analysts have the power to do far more than they ever could.
While no one says big data anymore, its concepts touch other hot topics such as elastic search development, cloud data management and cloud development. From the business transactions that fuel an organization's operations to consumer information that can be accessed and analyzed for marketing purposes, businesses today are practically wading in information and tools to manage data.
Modern data platforms help organizations tame big data, avoiding overload and allowing businesses to maximize data usage. These platforms help organizations optimize cloud data management, for example, which results in a high degree of scalability for storage.
Modern platforms enable elastic search, which is also scalable and provides near real-time searching capabilities.
Big Data Transforms Enterprises
The benefit of big data is not in the sheer volume of information, but rather in how the data is used. Inefficient amassing of information can drag operations down. Some industries create or collect absurd amounts of data.
Big data benefits organizations that know how to leverage the information into actionable results. Customer and operations insights can fuel optimization and customization in ways that were never thought possible. The key is to maximize current technologies, so enterprises can derive as much insight out of data as possible.
Modern data platforms enable finding actionable insights through five core features. These are the Three V's: Volume, Velocity and Variety, along with Variability and Complexity. Each of these benefits of modern data management helps enterprises add value to information.
Managing data volume
The sheer volume of possible and actual data is immense. Modern data technologies, such as Hadoop and other distributed data platforms allow for cloud data management that can ease the burden of big data. Prior to cloud development, today's data sets would cripple the old infrastructure.
Distributed data management can help an enterprise contain this information.
Speeding up data analysis
Operational speed is also crucial to modern databases. Volume and server overload causes search latency. Elastic search and other technologies can help organizations achieve real-time results. Enterprises are increasingly taking advantage of devices and sensors to provide instant information.
RFID tags fuel inventory management. Smart metering provides crucial real-time data. Modern data management has to be quick, or at least give the right velocity when needed.
Data variety means one solution does not fit all
Modern data can be structured - such as traditional numeric data sets or unstructured such as text, email, video, images and more. Enterprises need systems that handle this variety of data efficiently.
Data traffic is also variable
In addition to the Three V's of modern data management, information can come in unpredictable or irregular surges. For example, if some news item spikes traffic on a particular site or quickly popularizes a search term, an enterprise needs the flexibility and adaptability to handle the data at that moment.
Data is increasingly complex
On top of the variety of data, it also comes from multiple feeds and can cause categorization and management issues. Complex data that comes from disparate sources can be challenging for database management.
When data comes from many different sources, systems are necessary so that your enterprise can understand how to process the data for optimal value.
Elastic, Hadoop and NoSQL Platforms
These five characteristics of modern data's unique challenges create the need for new management tools. Platforms have arisen to help enterprises maximize the benefit of available data. These platforms include Elastic search, Hadoop -for building huge data lakes, and NoSQL.
These three platforms have risen to the top of the data management world due to their individual abilities to create high-volume, responsive data environments that provide maximum business benefit.
Elastic search, Hadoop and NoSQL are advanced analytics systems.
They allow enterprises to use custom queries and handle huge amounts of data. Each of these has its benefits and disadvantages. These platforms and tools have become popular because, for most enterprises, one of them will provide optimal results.
Elastic is a search engine, so its utility is in its speed with queries. It provides a distributed, scalable and enterprise-grade search engine. It is open-source, too, so elastic search development and optimization is always occurring. Elastic search is an improvement over some of the limitations of conventional SQL.
Conventional SQL isn't designed to handle full-text searches optimally. Elastic search allows users to quickly search text and other databases using simple yet powerful API, Query DSL, which provides data persistence.
Elastic search's scalability means that you can handle thousands of servers, but since it is fast and distributed, users are often unaware of its vast capabilities. Azumo elastic search developers know how to create scalable search engines that leverage these benefits and more.
Hadoop distributed data
Hadoop is a scalable distributed data platform. It is also open sourced and managed by Apache. Some of its technology has roots in a Google project and is a free software package for distributed data management.
The Apache Hadoop ecosystem hosts several modules that extend its functionality, but the Hadoop Distributed File System (HDFS) and MapReduce are at its core. The HDFS can be local or shared, and MapReduce enables the parallel processing that is key to a scalable distributed database management system.
In simplest terms, Hadoop allows for speed and efficiency by breaking a load across parallel processors running smaller queries. Then the distributed smaller queries mesh back together as a full dataset return. It is a highly effective manner of breaking large jobs into small, manageable tasks.
NoSQL is not SQL
NoSQL is a horizontally scalable non-relational database system. In SQL, tables dominate how data is managed. With traditional relational database structures, speed results from optimizing the relationships between tables. NoSQL abandons the concept of tables for objects. While objects may be easier to understand from a development perspective, they tend to make data management more complex.
SQL servers have limitations. Their traditional structure cannot handle the volumes of data present in modern computing. Tables do not translate effectively to other servers, so distributed database management is impractical.
NoSQL helps enterprises achieve automatic elasticity. If more processing power is required, additional servers can be added without downtime.
These three choices each also differ from traditional relational databases, which store data in a Structured Query Language (SQL) in a tabular or vertical orientation. Big Data's volume of information overly taxes relational databases, which requires the use of a more distributed system.
Types of NoSQL Databases
There are more than just one type of non-relational databases. There are generally four database categories: Key-Value, Document-based, Column-based and Graph-based. Each has unique features and utility.
Key value stores are the simplest of the four. In essence, each item in the database is assigned a key and its value. Simplicity is a benefit here, but there are several drawbacks. First is how there are few carried-over benefits from traditional relational models, such as consistency when dealing with multiple simultaneous transactions.
Also, key labels can get unruly as data sets grow more substantial, which can make a system overly complicated and inefficient. Popular key-value databases include Riak, Voldemort and Redis.
Document store NoSQL databases are similar to key-value databases but allow more complex data structures. Documents contain various compressed key-values. Documents are a key differentiation between traditional SQL and NoSQL.
The non-structured data contained in a NoSQL document is easier to manage and retrieve. MongoDB is the most popular of the document-based databases.
When data is organized in columns rather than rows, access speed is increased. The columns are optimized for larger datasets, with information queries requiring fewer steps. Cassandra and HBase are the most common column-based NoSQL databases.
Rather than a column or row structure, a graph-based NoSQL database uses a network that is represented through edges, nodes and properties that provides index-free adjacency. Popular examples of a graph-based database include Neo4J and HyperGraphDB
How Internet Giants Store and Retrieve Data
No case use exemplifies the need for non-relational database management more than search engine giants and social media titans. Facebook, Google and others deal with massive amounts of data. In order to efficiently handle billions of queries, these internet giants use distributed data systems.
Facebook uses globally distributed data centers, for example, because it needs to provide quick response to users all over the world. Since servers are physically remote, the databases are managed with sharding - a type of horizontal scaling that works with sharing massive databases among multiple servers.
Google has its own versions of NoSQL, through its Cloud Development and Cloud Data Management activities, such as CloudSpanner, that combines some of the benefits of a traditional relational database and a distributed system.
These companies needed to store and retrieve data differently and more cost-effectively than as would occur through a traditional SQL database.
How the CAP Theorem Complicates Database Matters
In an ideal world, a system or platform would be available that would address every issue and concern about your database perfectly. However, since each of the benefits of a distributed database can take away from another feature, a Theorem was advanced over 15 years ago to explain the limitations and provide a system for database architects and system engineers to choose from which features are most desirable.
The CAP Theorem is utilized to help enterprises choose the right data manipulation and management tools. It is a tool to aid in determining trade-offs. In Distributed Database Management, you are limited by the CAP Theorem.
This theorem is the idea that out of the three available guarantees - Consistency, Availability and Partition Tolerance - you can only have two available to your database. When designing these systems, it is important then to know what your needs are, so you can maximize the database for your use.
Let's take a closer look at each of the guarantees and how certain combinations may be available for certain uses.
A high-level of consistency is an ideal situation for many databases. As relates to a database structure, consistency is a fairly straightforward concept. Users want consistent results for data queries. In a distributed cluster, nodes should return the same data from the most recent write.
The same result should occur if User A searches the database as when User B does the same search. As regards CAP, the ideal consistency is considered linear or sequential.
High availability may also be ideal for certain database applications. These databases are built to continuously function as desired even during hardware or network failures. This is achieved through data replication across a distributed database.
Availability can be a challenge to cloud data management.
A partition tolerant database is one in which the systems persevere in the face of delays in message delivery between nodes. Partition tolerance means functionality during periods of near-total network failure. As long as part of the database is connected, the system as a whole functions in a partition tolerant scheme.
Most people can see how all three of these conditions are desirable. An ideal database design would feature each, but, the CAP Theorem states that to get maximum effect out of each, only two at a time can be optimized.
Since you can only have two of the properties at once, there are ultimately three categories that enterprises can choose from:
CP (Consistent and Partition Tolerant)
In this combination, the system sacrifices availability in the case of a network partition.
CA (Consistent and Available)
These systems are consistent and available when partitions are not present.
AP (Available and Partition Tolerant)
These systems are available and partition tolerant but can be inconsistent.
Since the CAP Theorem has been around for almost two decades, there have been some questions about the applicability of its limitations. Envisioning a partition intolerant database is difficult.
How Modern Data Platforms Solve Common Enterprise Problems
Case studies can shed light on the value of modern data platforms and how some of these concepts translate into real-life situations. Hadoop's distributed database management platform and MongoDB's NoSQL are just two of these tools.
Hadoop helped the Intercontinental Exchange Achieve Scale
The Intercontinental Exchange handles global futures and equity options exchanges. It is a crucial component of the global financial system and it creates massive amounts of data. When this data began to grow unwieldy, the exchange experienced poor data management performance that led to siloing of data as its data lake continued to grow.
The exchange deals with 20 PB of data. An Apache Hadoop deployment helped shore up this rampant data, resulting in real-time access and incorporation of machine learning as an analytic component.
How MongoDB helped HSBC
HSBC is a global bank that, like the International Exchange, handles a lot of data. Its trading technology was hampered by its sheer size. An implementation of MongoDB NoSQL modern database management helped build an Operational Data Store that will assist users in accessing complex data.
The MongoDB solution resulted in faster, more accurate and simpler database management. The distributed database management structure also resulted in increased overall operational efficiency. Rather than work in a siloed manner, teams at HSBC began working in a cross-functional manner, improving operations.
How to Deploy Distributed Database Management Systems
After reviewing the different database modes, one may be sold on their respective benefits but may wonder what method of deployment is best - on-premises or the cloud as part of cloud data management.
Should you use Elastic, Hadoop or NoSQL as an on-premise deployment in the cloud? The methods are highly effective. Your choice simply depends on your use case and your budget parameters.
For example, on-premises, a Hadoop platform can be cost-prohibitive due to its large server infrastructure requirements. However, it may be the most secure, as your database can safely reside behind a firewall.
For cloud data management, a platform such as Qubole is a self-managing and self-optimizing data platform. This tool allows for cloud-based access to insights, so the efficiency of your distributed databases can be monitored, and big data can be fully appreciated.
Qubole is also a highly-secure platform, utilizing end-to-end encryption, SOC Type II, global availability zones, virtual private clouds and role-based access controls.
Modern database management requires scalability and certain trade-offs. No system is perfect, but a distributed database system harnesses the power of vast connected server networks. With big data getting bigger all the time, it is crucial to manage and leverage it for optimal results.
Big data is, after all, not defined by how much information an enterprise collects, but rather, how it turns information into value for the enterprise.
Azumo elastic search developers can help you reign in vast amounts of data through intelligence and responsive database searchability.