6 Things to Keep in Mind When Choosing an Ideal Server for Big Data Requirements

Big data refers to a massive volume of data sets that can not be processed by typical software or conventional computing techniques. Along with high volume, the term also indicates the diversity in tools, techniques, and frameworks that make it challenging to tackle and process the data. When stored and processed properly, this massive data can offer deep insights to the businesses. There are a number of ways in which big data can help businesses grow at an accelerated rate.

How Can Businesses Benefit From Big Data?

The businesses can store and process high amounts of data from diverse internal and external sources. Like company databases, social networks, and search engines to get excellent business ideas. It can also allow them to forecast the events that can have a direct impact on business operations and performance. On the marketing front, it can help you increase the conversion rate by offering only relevant schemes, launches, and promo offers to the customers based on their buying behavior. The progressive companies are using big data for new product development, understanding the market conditions, and utilizing the present and upcoming trends for direct business benefits.

The Role of Server in Big Data

For enjoying the optimum business benefits out of big data it’s important to choose the ideal hardware that can proactively assist in big data operations without significantly inflating the costs or complications. There are some challenges to address like determining the processing requirements, high volume data storage at superfast speed, and supporting simultaneous computations of massive levels without compromising with the output. An important part of this strategy is to choose the right type of server. 

The standard servers generally lack the resource volume and technical configuration required for various big data operations. So you would need the premium, purpose-built servers that are specially tailored to accommodate the massive data volume. As well as support the computational, analytical, and processing tasks. However, the final decision should be based on your specific requirements as no two customers are the same. You can find additional information on big data hosting in this previous article

In this blog we are going to present some of the ideal factors to keep in mind while deciding on the ideal server for ensuring optimum big data benefits:

1. Choose Servers with High-Capacity

The ideal properties of a big data server are massive storage, ultra-fast recovery, and high-end analytical capability. So, you need the servers that have the right configuration and capacities to meet all these requirements without any compromise.

  • Volume. As the name suggests, the big data feeds on loads of data that can go up to petabytes. For the uninformed, a single Petabyte is equal to 1,000,000 GB. So, make sure that your server can not only handle this massive amount of data but can also continue working consistently while handling it.
  • Real-Time Analysis. The USP of big data is organizing and structuring a huge volume of diverse and unstructured data and seamlessly adding the latter to the available structured data. So, you would need the servers with very high processing capacities to handle this requirement efficiently without fail.
  • Retrieval capabilities. Big data has big objectives too. For instance, real-time stock trading analysis where even a fraction of seconds matters a lot and can introduce multiple changes. For that, your server should fully support multiple users who are concurrently adding multiple inputs every second.

2. Sufficient Memory

RAM is one of the prime requirements for big data analytics tools and applications. Using RAM instead of storage will significantly accelerate the processing speed and help you to gain more output in relatively less time. It translates to better productivity and quicker time-to-market – the two factors that offer you a competitive edge in the industry. Due to varying requirements in terms of volumes and operations, it is not possible to advise on a typical RAM volume. However, to be on the safer side it is good to go with at least 64GB RAM. The readers are advised to discuss their requirements with the providers to know about the ideal memory requirements for their purpose.

3. Better RoI with NoSQL Databases, MPP and MapReduce

You also need to assist your clients in neatly segregating the analytical and operational requirements. It requires wisely optimizing the server hardware to meet the purpose. It is best to go for the NoSQL databases.

Unlike traditional databases, the NoSQL databases are not limited to a single server but can be widely spread across multiple servers. It helps it in dealing with tremendous computations by multiplying its capabilities manifolds and instantly scale up to the changing requirements in a fraction of seconds.

NoSQL databases can be defined as a mechanism that doesn’t use the tabular methodology for saving the data. Its non-relational data storage technology efficiently helps the businesses overcome the limitations and complexity inherent in traditional relational databases. To the end-users, this mechanism offers high speed scaling at relatively very less cost.

To accelerate the analytical big data capabilities you can rely on MPP databases (massively parallel processing) and MapReduce. These databases can significantly outscale the traditional single severs. You may also look for the NoSQL systems with inbuilt MapReduce functionality that allows it to scale to the cloud or a cluster of servers along with NoSQL.

4. Sufficient Network Capacity

You would need to send massive data volumes to the server. Lack of sufficient network capacity can throttle your operations. Be considerate of the fluctuations as well. You wouldn’t regularly be writing huge data volumes, which means that buying high bandwidth plans isn’t a cost-efficient solution for you. So, opt for the bespoke bandwidth solutions that allow you to select the ideal bandwidth to competently fulfill your data transfer requirements.

You can choose different bandwidth packages starting from 20 TB and going up to 1000 TB per month. To make things easier you may like to inform your provider about your expected data transfer requirements and ask them about the ideal bandwidth volume. Reputed providers can also offer you unmetered bandwidth for more demanding enterprise clients. Depending upon the volume and frequency of data 1Gbps is the least amount of bandwidth you require for your server.

5. Purpose-Specific Storage Capabilities

Along with storing permanent data your server also needs to accommodate huge amounts of intermediate data produced during various analytical processes. So, you would need sufficient data storage, Instead of choosing storage based on their capacity, think about their relevance for your purpose. The reputed vendors would always suggest you check your requirements before buying the storage. For instance, investing huge amounts on expensive SSD storage doesn’t make sense if your data storage requirements are modest and the traditional HDD can solve your purpose at much lower prices. 

6. High-End Processing Capacity

The analytics tools related to big data generally divide the processing operations across different threads. These threads are distributed across different cores of the machine and are executed simultaneously. For a modest, to average load, you need 8-16 cores but may require more than that depending on the load. The rule of thumb is to prefer a higher number of cores rather than a smaller volume of highly powerful cores if you are looking for more competent performance. 

Should You Use Software for Server Optimization to Meet Big Data Requirements?

The big data ecosystem has very specific needs that standard data servers with limited capabilities in terms of multitasking, output, and analytical insights can’t support. It also lacks the ultra-speed needed for real-time analytical data processing. So, you would require bespoke enterprise servers that seamlessly adapt to your particular needs in terms of volume, velocity, and diverse logical operations. For massive big data operations, you may need white box servers.

While technically it’s possible to employ software for optimizing the server environment. It may prove to be an expensive option in the long run by significantly reducing the RoI.

It also exposes your system to various security risks while at the same time increasing the management hassles like license acquisition/maintenance, etc. Moreover, you would have limited opportunities to fully utilize the available resources and infrastructure. 

On the other hand, using a purpose-specific server for the big data requirements offers multiple benefits like:

  • More operations per I/O that translate to better computational power 
  • Higher capabilities for parallel processing 
  • Improved virtualization power
  • Better scalability
  • Modular design benefits
  • Higher memory
  • Better utilization of the processor

Additionally, specially tailored servers can smartly work in collaboration. To assure the best possible utilization, virtualization, and parallel processing requirements. Due to their specific architecture, it’s easier to scale and manage them.

Conclusion

Big data can help your business grow at a very high rate. However, in order to get the best benefits out of your big data strategy, you need to build a purpose-specific ecosystem that also includes ideal hardware.

So, we mentioned some major factors to keep in mind while choosing the ideal server for your big data requirements. And now it’s time for you to let us know in the comments section below how do you think you can benefit from it. We want to hear from you!

NoSQL vs SQL: Examining the Differences and Deciding Which to Choose

NoSQL vs SQL

At 74, Larry Ellison, co-founder and CTO of Oracle has amassed a fortune of $66.1 billion. He got going in 1966 and in the seventies took an idea from IBM’s Edgar F. Cobb for a SQL relational database. This became the Oracle Database rdbms (relational database management system). With no free software competitors, Oracle totally dominated the market. Everything else, like DB2 was running on IBM mainframes, and even it couldn’t oust Oracle from its top position. Mainframes remained popular until the 1990s, when PCs started to be used as servers, as they still are today. Oracle is still in the top spot for the majority of transactional business applications used by the richest companies. It bought the commonest opensource, MySQL, along with opensource Java, but both are still free to use. The big choice for all companies is still SQL vs NoSQL – between relational (SQL) or non-relational (NoSQL) data structure. Both are great in their own way, and both come with pros and cons of course, which we’ve listed for you here.

What is SQL?

SQL (Structured Query Language) organizes information in relational databases. It’s used in Oracle, Sybase, Microsoft SQL Server, Access, and Ingres. SQL uses relations (usually referred to as tables) to store and match data using shared features within the dataset.

It was Cobb’s notion that you could have a database that could be queried using a structured query language. He used SQL to create data in objects known as tables, along with the schema for that data, which describes fields in columns. One SQL record is known as a row.

What is NoSQL?

A NoSQL database describes itself, so it doesn’t need a schema. It also doesn’t mandate relations between tables in all scenarios. It only uses JSON documents, which are self-contained and easy to understand. NoSQL means high-performance, non-relational databases that use many different data models. They are known to be easy to use, have scalable performance, are resilient, and also widely available. NoSQL database examples include MongoDB, MarkLogic, Couchbase, CloudDB, and Amazon’s Dynamo DB.

NoSQL vs SQL: Major Differences

When choosing a data management system for your organization, you need to take into account the many and varied differences between SQL and NoSQL. There are differences in:

  • Language
  • Scalability
  • Community
  • Structure

Language

Use of a Structured Query Language makes any SQL-based database very versatile and helps to explain why it is used so widely. On the downside though, this also restricts it. You have to use predefined schemas to set out the structure of your data before you can even get started. Your data has to use the same structure too, structure as well, you may have to invest considerable time into pairing your data to make it ready.

A NoSQL database has a dynamic schema for unstructured data which can be stored in a lot of different ways, including graph-based, document-oriented, column-oriented, or organized as a KeyValue store. Being highly flexible like this means you won’t be burdened with the same amount of preparation. You’re free to add fields as you go and vary the syntax from database to database. Every document can have its own individual structure, so have a great deal of latitude.

Scalability

Another significant difference between SQL and NoSQL is how scalable they are. With the majority of SQL databases, can scale them vertically, meaning individual servers can be boosted through the addition of more RAM, SSD, or faster CPU. But NoSQL databases scale horizontally, meaning that they can handle increased traffic simply by adding more servers to the database. NoSQL databases have the ability to become larger and much more powerful, so they are great for handling large or constantly evolving data sets.

Community

SQL has been around for long enough now that its community is large and well developed. If you need a query answered or want to pick up new skills, there are seemingly endless forums full of experienced users who will be glad to help you out. NoSQL can’t match this level of peer support yet because it’s the new kid on the block, so unfortunately, you’ll have to come back in a few years.

Structure

SQL databases use a tables approach which makes them better suited to handling apps that ask for multi-row transactions. Accounting systems or legacy systems that were originally created for a relational structure are examples of these. NoSQL databases can be key-value pairs, wide-column stores, graph databases, or document-based.

SQL or NoSQL: Which One is Going to Fit Your Business?

The best way to determine which database is right for your business is to look at what you need it to do. If you need a predetermined structure, multi-row transactions and set schemas then SQL is the one to go for. It’s also highly consistent, which makes it an ideal choice for accounting systems.

If your company is growing rapidly and doesn’t need clear schema definitions, then NoSQL is what you want. A relational database won’t offer as much flexibility as NoSQL, which is great for companies that need to churn through large amounts of data that comes in varying structures.

Examples

We can see that the first field is teacher and the second field is subject.

{ teacher:  "James Smith", subject:  "literature" }

With SQL, you create this schema before adding it to the database:

CREATE TABLE teacherSubjects (
teacher varchar,
subject varchar
);

Varchar is variable character length. To add data to that table, you would:

INSERT INTO teacherSubjects (teacher, subject)
VALUES ("James Smith", "literature");

With a NoSQL database, in this example using MongoDB, you would use the database API to insert data like this:

db.teacherSubjects.insert( { name: "James Smith", subject: "literature" } )

Now you can create the union (all elements from two or more sets) and intersection (common elements of two or more sets) of sets using SQL.

This was such a big deal because all this could be programmed using simple SQL syntax. Then Oracle added indexing fields and caching records to improve performance and make sure that the database could complete referrals with integrity. (Referential integrity is about the completeness of transactions, so you aren’t left with orphaned records. For instance, a sales record with no product to go with it. This is what enforcing the relationship between tables refers to in Oracle.)

Note that in the above MongoDB example, Oracle programmers would call the teacherSubjectes table an intersection. It tells you what subjects a teacher has and also which teachers are in which subject. So you could also add things like subject room number and teacher email address to both records.

The Oracle database is known as a row-oriented database because that’s how it’s organized. There’s no need to turn our attention to column-oriented databases like Cassandra here, because they have different architecture. So, they are not so fundamentally different as SQL vs NoSQL. In particular, the Cassandra NoSQL database columns with similar data near to each other for the fastest possible retrieval. Cassandra and NoSQL databases do away with the concept of database normalization, which is fundamental to Oracle, as we outline below. And they don’t keep empty column values, so the rose can be different lengths.

Normalization and Efficiency

Something which Oracle emphasized was the relationship between objects, insisting that all data should be normalized, and nothing should be stored twice. In practical terms, instead of repeating the school address in every teacher record, it would be more efficient to keep a school table and put the address there. This constraint is largely absent in NoSQL databases, so it wins out here in the SQL vs No SQL debate.

Storage space and memory were costlier in the 1970s, so normalization was necessary. These days though, assembling a record that is split between different tables takes more of both, not to mention the fact that you also need to maintain index files, which can slow everything down.

Fans of NoSQL databases say memory and storage are so cheap and processing power so exponentially faster now that none of that really matters. The computer can handle it and it’s easier for programmers to code.

NoSQL vs SQL

SAP is Oracle’s biggest business competitor and has its own database, Hana. Oracle keeps all its records in memory (flushing them to disk as necessary) for the speed advantage it brings, but apart from that, they work in pretty much the same way.

NoSQL has been around for so long that it’s hard to argue a business case for changing to a newer one. When firms already understand rdbms, why switch? Oracle has solved management issues like data replication, which might leave someone using, ElasticSearch, for instance, unsupported with a compromised system on their hands. To avoid this, some businesses support opensource databases, like ElasticSearch, in-house, so you can buy in the help you need from them.

There’s been a big shift towards transactional systems. The addition of a sale to a sales database is an easy-to-understand concept. Once it’s done, Oracle calculates on-hand inventory using a saved SQL operation called a view. For MongoDB, a program would have to go through inventory items and takeaway the sales to work out the new on-hand inventory.

NoSQL Databases in Action

Looking at MySQL vs NoSQL, it’s interesting to note that NoSQL databases tend to be used in niche rather than enterprise systems. Uber is a good example as it uses Cassandra to keep tabs on its drivers, but it has unique needs, like writing millions of records a second across many data centers. The company wrote its own version of Cassandra in order to have it run on Mesos. Mesos is an orchestration system that resembles containers.

Amazon markets is a DynamoDB database which has “millisecond latency.”

DynamoDB, like MongoDB, has a JavaScript interface, which makes it simple to use. To add a record, for instance you open the database, then add a JSON item like this:

var docClient = AWS.DynamoDB.DocumentClient()
docClient.put("{JSON … }"}

One implementation detail is that you can use Node.js to run these operations in MongoDB and DynamoDB. Which means JavaScript running in the middle tier, so you don’t have to create JAR files or middleware servers like Oracle Weblogic.

So, which of the two is best for you? You could still run your accounting system on a RDBMS system. But don’t necessarily need to pay licensing fees to Oracle. You could use MySQL instead. But will it use MongoDB? That is unlikely in the short term, as huge numbers of programmers across the globe use Java and Oracle, which project managers and users understand. Use ElasticSearch for logs and Spark for analytics. With the others, look at them individually to see if they will fit in with your resources, skills, tolerance for suffering lost transactions, etc.

Conclusion

Whatever your field, selecting the correct database for your firm is a crucial decision. NoSQL databases are rapidly establishing themselves as a significant force in the database landscape. They bring many benefits: they are cheaper, are open-source, and easily scalable, which makes NoSQL more appealing for anyone who needs Big Data integration. It’s a new technology though, which can bring its own problems.

SQL databases, in contrast have had more than four decades to establish their well-defined. A mature community offers almost limitless possibilities for collaboration and support.

In the end, the choice of SQL vs NoSQL for business will come down to the individual needs of the companies concerned. Only through extensive research comparing their abilities to your needs will you find the one that is the best fit.