Big Data Companies

Choosing the Best Big Data Companies

A Detailed Evaluation Criteria

For the purpose of selecting the best companies providing big data solutions, we’ve designed an end-to-end evaluation methodology. After compiling the initial list of prospective firms, we went through their websites and past works. Additionally, we considered the solutions they offered and the proficiency of each company’s team members. Below, you can find detailed information regarding the criteria we employed.

The Company’s Website & Portfolio

Our evaluation of these data analytics firms starts with a website visit. We look into each agency’s past projects and got through their case studies to try and establish their experience and specializations. Additionally, we check how long they’ve been on the market. Although a newly founded startup can deliver outstanding services at times, we favor companies that have more years of experience in the matter. 

Big Data Services

Big data involves different services, and not all companies deal with all of them. Some companies listed here specialize in one or maybe a few while others provide a full service. Here are the services we take into consideration:

Developing Big Data Architecture

Big data architecture is the blueprint used to process the big data so it can be analyzed for business purposes. Essentially, it defines how the big data solution will work, the components used, as well as the flow of information, security, and more. A robust big data architecture can save businesses money while also helping in the prediction of future trends.

The top analytics companies on our list are verified as having the right skills to craft big data architectures through the following processes:

• They can effectively define client objectives.
• They’ll consult on the most efficient solutions.
• They can plan and execute a complete computing network, while always keeping in mind the most appropriate hardware, software, data sources, and formats, and data storage solutions.

Big Data Consulting

There’s more to consulting on big data than advising companies on the most efficient strategy and its implementation. To make it on our list, companies offering this service are verified for the following:

• They have advanced technical knowledge and proficiency in a variety of big data tools for specific processes, from data acquisition and warehousing all the way to data modeling and visualization.
• They can offer a strategic solution for the collection, storage, analysis, and visualization of data from various sources and for a variety of purposes.
• They exhibit excellent team-leading and collaboration skills, which are necessary when working with companies’ in-house teams.
• Analytics firms must all keep up with the latest trends to include the most efficient and effective solutions within their service catalog.

Data Acquisition

As a process that encompasses collecting, filtering, and cleaning data before putting it in a warehouse or other storage solution, big data acquisition needs to satisfy the five Vs:

• Volume, which refers to the large amount of data produced and shared every second
• Velocity, which concerns the speed of data generation and movement
• Variety, which relates to the different types of data that can be used
• Value, which refers to the creation of utility from big data, based on the desired outcomes
• Veracity, which refers to uncertain data.

Usually, data acquisition assumes high volume, high velocity, high variety, but low-value data. This highlights the importance of adaptable and time-efficient gathering, filtering, and cleaning algorithms. These ensure the data warehouse analysis process only covers high-value data fragments.

To make sure that happens, we look for big data solutions companies that follow a specific performance pipeline. This involves the process of acquiring, validating, cleaning, deduplicating, and finally transforming the data. 

Additionally, we make sure that the chosen companies adhere to these key principles: 

Asynchronous Data Transfer

Asynchronous data transfer moves one character or one byte at a time, sending data in a constant current of small bits instead of a solid stream. There are two ways to implement this system: by using a file-feed transfer or using a MOM (message-oriented middleware) to deal with the potential back pressure from data being generated faster than it’s consumed. 

Data Parallelism

The top big data companies know that using the right parser is one of the most critical factors in optimizing data formats for the APIs implemented by the MOM. Data transformation requires the most time and resources, so using data parallelization to transform it before processing is essential at this point. Another option is to filter duplicate data earlier in the process.

Technologies

Besides ticking the above-mentioned points, the leading data analysis companies need to use the latest and greatest methods and technologies, such as the following:

• Apache Kafka – This open-source streaming platform is based on an abstraction of a distributed commit log. It’s able to handle trillions of events per day.
• ActiveMQ – This messaging software serves as the foundation for an architecture of apps that were built on messaging.
• Amazon Kinesis – This Amazon data stream processing solution is capable of processing hundreds of terabytes an hour from high volumes of streaming data. 
• Akka Streams – This open-source library processes and transfers elements based on Akka.
• RabbitMQ – This messaging broker gives apps a common platform for sending and receiving messages.

Other technologies include JBoss AMQ, Oracle Tuxedo, and SonicMQ.

Types of Data

We also expect the prime data analyst companies to be able to generate different types of data, which include the following:

• Structured Data – This refers to the highly organized data that can be seamlessly processed, stored, and retrieved in one set format. 
• Unstructured Data – This data has no structure, which makes processing and analyzing it very difficult. 
• Semi-Structured Data – Semi-structured is a combination of the previous formats. This is data that’s been classified under a specific database but still contains important information for separate individual elements within the data.

Data Warehousing

Depending on your goals, data warehousing might have certain limitations as a standalone solution. This is because a data warehouse is essentially a repository, while big data is a technology that handles data and prepares it for the repository. Unlike big data, a data warehouse solely handles structured data. 

The top big data analysis companies that offer this service and make it onto our list follow the latest best practices:

• They adopt a recognized data warehouse architecture standard to enable efficiency within a chosen development approach.
• They follow an agile data warehouse methodology to break down projects into smaller pieces, which can be delivered faster and return value more quickly.
• They use a data warehouse automation tool to help leverage IT resources to the fullest and enforce coding standards.

Data Modeling

Simply put, data modeling denotes the sorting and storing of data. As big data runs on non-relational databases, one might assume it doesn’t need modeling. Quite the contrary, modeling is crucial to big data analytics success. We verified that the big data companies on our list mind the performance, quality, cost, and efficiency of data models. 

The Best Data Modeling Methodologies

• The ER Model thematically sorts data from an organization-wide perspective, instead of targeting data batches specifically related to a certain process. The sorted data needs further processing for analysis and decision-making.
• The Dimensional Model sorts data related to a certain event, a state of an event, or a process (including a series of related events) and enables a high-performance analysis when handling large and complex queries.
• The Data Vault Model is scalable and most useful for data integration; however, sorted data can’t be used for analysis and decision-making as it is.
• The Anchor Model provides the highest scalability of them all; however, to achieve that, this model increases the number of join query operations.

Data Integration

We list the business intelligence companies that understand the importance of quality over quantity. Data needs to be used in the context of your business, so these companies must be able to find the most relevant subset to integrate with your historical data and properly serve your BI initiatives.

Based on the type of data processing they offer, here are some additional capabilities companies must have to make it on our list:

Batch Processing

As the name suggests, this refers to the periodic processing of blocks of data that have already been stored over a specific period of time. Hadoop MapReduce is the best framework a company can follow to do this.

Stream Processing

As opposed to the above, big data companies use stream processing when they need data integration within short periods of time upon data arrival. By short we mean seconds and even milliseconds. This is why it’s also known as real-time processing. When it comes to the best practices, they mostly depend on the individual definition of real-time. As far as platforms go, we’re looking for Apache Kafka, Apache Flink, Apache Storm, Apache Samza, and others that can pull and process data from various sources.

Reporting

The data analytics companies on our list that offer reporting among their services are efficient in the following:

• They can segment data for the parameters you need reporting on.
• They can build a new model or build on top of an existing model.
• They’ll apply the best practices based on whether real-time or batch processing is needed.
• They will visually present the results for the above.

Data Visualization

The essence of big data processing lies in its ability to present processed data in a graphic format that’s easy to understand and interpret. This goes beyond graphs, tables, and pie charts. More often than not, the loads of data processed and presented are massive and require comparisons based on a variety of parameters.

On our list, you’ll find the big data analytics companies that are efficient in the following types of data visualization:

• 2D/Planar/Geospatial – These include cartograms, dot distribution maps, proportional symbol maps, and contour maps.
• 3D/Volumetric – These are 3D computer models and computer simulations.
• Temporal – This includes timelines, time series charts, connected scatter plots, arc diagrams, and circumplex charts.
• Multidimensional – These could be pie charts, histograms, tag clouds, bar charts, treemaps, heat maps, and spider charts.
• Tree/Hierarchical – This includes dendrograms, radial tree charts, and hyperbolic tree charts.

Specifically, they will be able to deliver data interpretations in the form of:

• Kernel density estimations for non-parametric data
• Box and whisker plots for large data
• Word clouds and network diagrams for unstructured data
• Correlation matrices
• Scatter plots

Each Company’s Approach to Big Data Analysis

The next thing we assess is the approach each data analytics company takes in the analysis process. Here’s what we consider:

Descriptive Analytics

A conventional form of business intelligence, it involves describing raw data in a way that is interpretable to humans. It is highly useful as it enables businesses to learn from their past and understand how they might shape future results. Businesses may need it as a standalone service, or as preparation for predictive or prescriptive analytics. We made sure that the top data analytics companies on our list are proficient in data mining and aggregation and presenting it in an easy-to-digest format.

Predictive Analytics

As the name itself suggests, predictive analytics “predicts” what might happen next and provides actionable insights along with estimates of the probability of potential future outcomes. Although not considered a core benefit to big data, predictive analytics as a service should be as reliable and accurate as possible. 

Even though this will greatly depend on the quality and veracity of the data subject to analysis, we made sure that the big data analysis companies on our list follow the best practices on this front as well:

• They can establish the right metrics needed.
• They can find the most efficient data source.
• They can establish a simple data processing model that aligns well with the existing one.
• They make sure the model is scalable and testable.
• And finally, they deliver simple and visually clear predictions.

Prescriptive Analytics

Prescriptive analytics is a relatively new field of big data that uses data of both descriptive and predictive analysis to determine which is the best possible future scenario. The data science companies you’ll find here are proficient in the latest technologies, such as machine learning and AI, which are mandatory for successful prescriptive analytics.

Diagnostic Analytics

The main purpose of this type of analytics is to determine the cause of certain events. Companies on our list offering this service are verified for their proficiency in not only data mining and discovery but also drill-down and correlations. Moreover, our listed big data companies need to be proficient in the following:

• They can use descriptive analytics to identify anomalies.
• They can identify data sources to establish patterns outside of existing datasets.
• They use data mining to identify correlations and verify if any of them are causal.
• They can use probability theory, regression analysis, filtering, and time-series data analytics to uncover the “hidden” events that caused the initially identified anomalies.

Technologies Used

Our next step is to verify whether the companies keep up with the latest big data technologies. Depending on the services they offer, we check for the big data analytics companies’ proficiency with the following technologies:

• The Hadoop Ecosystem is an open-source framework for processing large data sets. It comprises a number of services, from consuming and storing data to analyzing and maintaining it. 
• Apache Spark is an engine for processing big data within Hadoop. It’s faster and more flexible than MapReduce, which is the standard Hadoop engine. 
• R is an open-source programming language exclusive to big data, supported by many integrated development environments.
• Data lakes are repositories that gather data from various different sources and store it in its unstructured state.
• NoSQL databases—like MongoDB, Cassandra, Redis, and Couchbase—are more scalable than relational databases, provide superior performance, and specialize in storing unstructured data, ensuring a fast performance but a lower level of consistency.
• AI is an important part of effective big data analysis. When looking at historical data, machine learning can help recognize patterns, build modes, predict possible outcomes, and facilitate predictive analytics. Deep learning is a subset of machine learning and relies on artificial neural networks, using multiple layers of algorithms to analyze data.
• Deep learning is a subset of machine learning and relies on artificial neural networks, using multiple layers of algorithms to analyze data.
• Edge computing systems analyze data very close to where it was created—at the edge of the network—instead of transmitting data to a centralized server for analysis. This reduces the amount of information that transfers over the network, thus decreasing network traffic and its related costs. It also decreases demands on data centers or cloud computing facilities, freeing up capacity for other workloads and eliminating a potential single point of failure.

Storage & Backup Solutions

Business intelligence companies offering storage solutions and optimization must keep in mind the 3 V’s of big data storage:

• Variety, in terms of the sources and formats of the data being collected
• Velocity, in terms of the pace at which, said data is collected and processed
• Volume, in terms of the size of data being collected and processed

The perfect solution depends on a business’s data requirements:

On-Premises Big Data Storage

1. An Enterprise Network Attached Storage (NAS) Solution works with file-level storage capacity, which can be increased by adding more disks to existing nodes. However, since this practice can compromise performance, we looked for innovative companies that expand storage capacity by adding more nodes. This way, big data companies not only employ more storage space but also more computing capabilities.

2. Object Level Storage or Storage Area Network (SAN) replaces the tree-like architecture of file-level storage with a flat data structure. The data is located via unique IDs, which enables easier handling. Additionally, this architecture provides adeptness in addressing IOPS-intensive workloads.

3. A Hyper-Scale Storage Solution runs on the petabyte scale and is used by social media, webmail, and more. It relies on automation rather than human involvement, which in turn optimizes data storage and reduces the probability of errors. The potential downside is that it has a minimal set of features, as big data firms use it to maximize raw storage space while reducing the cost.

4. A Hyper-Converged Storage Solution can be scaled out horizontally by adding more nodes. This allows for a distributed storage infrastructure using direct-attached storage components from each physical server. These are thus combined to create a logical pool of disk capacity. The nodes within a cluster communicate via virtualization software, making all the data stored in them accessible through a single interface.

Cloud Big Data Storage

On our list, you’ll find the leading big data companies. Because they’re the best, they should be able to deal with the following when it comes to storage:

Private Cloud

If offering this service, companies should be efficient in providing a public cloud service to end-users. In other words, they need to provide the following:

• Elasticity – They can increase and reduce consumed resources as needed with little or no manual intervention from storage administrators or others in IT.
• Multi-tenancy – This covers the ability to support multiple clients (departments, divisions, offices, and sometimes individuals) at an equally consistent level of performance, while also preventing them from viewing and accessing each other’s data.
• Detailed billing reports based on consumption over time – These big data companies should know how to report and charge against individual departments, business areas, or teams.
• Maintenance and flawless operation –This involves establishing orchestration frameworks by using specific tools or cloud management platforms like Microsoft Azure Stack and the VMware vRealize Suite, or open-source platforms like Apache CloudStack and OpenStack.
• Robust management software – They can integrate computing and networking, thus enabling reporting and analytics.

Public Cloud

If big data companies offer this type of service, it actually relies on service providers like Amazon Web Services, Microsoft Azure, and the Google Cloud Platform.

Hybrid Cloud

The companies on our list that offer this kind of storage solution are skilled at separating sensitive data to be stored in the private cloud. When using a hybrid cloud solution for backup, the companies can efficiently separate sensitive and/or more frequently used data, which is backed up on the private cloud. The big data analytics companies will then segment the rest to be backed up on a public cloud.

Data Compression Solutions

Data compression is used to save disk space or reduce the I/O bandwidth used when sending data from storage to RAM or over the internet. There are two types of data compression:

• Lossless compression is mostly used when compressing high-quality files, usually multimedia. This type of compression enables the recovery of all of the original data when the file is uncompressed.
• Lossy compression, a.k.a. irreversible compression, removes data beyond a certain level of fine-grain detail. It’s typically used for text and data files, like text articles and bank records. 

For our list of the top data analytics companies, we’ve selected the candidates that are proficient in the former, since it’s important to keep every bit of data you have. We’ve also ensured they rely only on the latest and most advanced algorithms, like the following:

• Run-length encoding (RLE)
• Dictionary coders: LZ77 & LZ78, LZW
• Burrows-Wheeler transform (BWT)
• Prediction by partial matching (PPM)
• Context mixing (CM)
• Entropy encoding:
  • Huffman coding
  • Adaptive Huffman coding
  • Arithmetic coding
    • Shannon-Fano coding
    • Range encoding  

Security Measures

Hiring a cybersecurity expert can help you with security matters but the best big data company has to employ some effective techniques to make sure your data is safe from input to storage and all the way to the output stage.

Here are the practices top analytics companies should be able to employ:

• Protect distributed programming frameworks
• Secure non-relational databases
• Secure data storage and transaction logs
• Endpoint filtering and validation

In addition, the following covers the technologies we expect any good big data company to be skilled with:

Encryption

Encryption needs to be employed on the whole data load, both in transit and at rest, all data types, coming from all sources. It needs to be compatible with the RDBMSs and non-relational databases like NoSQL, as well as specialized file systems like the Hadoop Distributed File System (HDFS).

Centralized Key Management

Centralized key management focuses on management across the entire organization, where all users follow the same protocol. The best practices include policy-driven automation, logging, on-demand key delivery, and abstracting key management from key usage.

Granular User Access Control

Granular user access control requires business intelligence companies to follow a policy-based approach that automates access based on user- and role-based settings. In simpler terms, granular access control defines who can have access to different parts of a system and what they can do with it. In this case, multiple administrator settings can protect the big data platform against attacks from the inside. 

Intrusion Detection and Prevention

IPS enables security admins to protect the big data platform from intrusion. Should an intrusion succeed, IDS will quarantine it before it does significant damage.

Physical Security

Physical security refers to restricting access to strangers and unauthorized staff to data centers. These security systems include video surveillance and security logs.

An important thing you need to note is that both you and the big data company are equally responsible for implementing the appropriate security measures when it comes to the data.

Clients Reviews & Testimonials

In our evaluation process, client reviews and testimonials are an important ranking factor in determining the best data analytics companies. Beyond considering the testimonials found on the agency’s site, which usually show only the pretty side of the customer-company relationship, we make sure to scout third-party platforms and check what past clients have to say about each big data company. We take into consideration the good and the bad. However, unless the bad reviews outnumber the good, we don’t weed out the company. Instead, we use their comments to gain insight into a company’s weak spots. 

Things to Consider Before Closing the Deal

Having the best big data analytics companies list in front of you is just the beginning of your journey toward finding the right partner. In the following section, we break down the most important things you need to take into consideration before closing the deal.

What Solution Do You Need?

Big data encompasses a variety of services. Naturally, not all of the companies will offer all of them. Most of them specialize in one or two services, so on our list, you can find dedicated data analytics companies, as well as those whose services center around data acquisition, data modeling, data warehousing, etc. Determining what services you need will be your starting point for shortlisting candidates. 

Define the Source of Data Acquisition

If you don’t want to end up with loads of irrelevant big data, you need to define the source of data acquisition. The sources you can go with include the IoT, sensor network, social network data, data from mobile applications, open data on the web, data sets inside organizations, activity-generated data, legacy documents, and surveys. Based on what you’re looking to achieve, you can either choose a single one or a combination of two or more. This is a matter you should be able to discuss with your big data consultants.

What’s the Agency’s Experience Working with Companies in Your Niche?

Big data can be intimidating, but with the right solutions, your business can address the most important data and obtain actionable insights that will boost the value of your relationships with clients. Considering whether the prospective analytics firms have previously worked with a business in your niche is important. You get the opportunity to see firsthand how they handled similar types of data and whether their solutions will work for you. 

Do You Need Real-Time Data Processing?

Real-time processing includes continual data acquisition, processing, and output. The data is processed in a short period of time as soon as it enters the system. This type of data processing enables businesses to take immediate action, and it’s usually used in customer services, radar systems, and bank ATMs. If your business needs real-time data processing, make sure to find the companies on our list that offer this service.

Do You Need Big Data Architecture?

Each business needs different business intelligence services. If you need to process data sets over 100 GB, extract data from multiple and extensive resources, both structured and unstructured, or summarize and transform large amounts of unstructured data into a structured format for better analytics, then you will need data architecture. Knowing this will narrow your choice of agency.

What Kind of Storage and Backup Solution Do You Need?

There are several types of storage and backup solutions. Let’s take a closer look at the most popular ones offered by data analytics companies.

On-premise is far more expensive and demanding. It’s a physical platform that requires large numbers of servers, a large facility to house them, and large amounts of electricity to run them. 

Additionally, it requires on-site IT teams to make sure that everything runs smoothly. All of this is further increased if you also decide to back up data on-premise. The upside is that you have more control over the data, and this type of backup solution is considered faster and more secure from cyber data breaches. 

On the other hand, a top analytics company is likely to prefer the cloud because it’s more scalable and far cheaper. The downside is that it solely relies on an internet connection, so a small glitch can inhibit data processing. Moreover, it often limits management and maintenance. 

There are several types of backup. Each is beneficial in the specific circumstances outlined below: 

• Private cloud storage and backup are good if you’re dealing with data that’s sensitive in legal, compliance, or security. However, it’s limited in terms of scalability and requires dedicated staff.
• Public cloud storage is significantly cheaper and very flexible for scaling. It might also appeal to big data solution providers because it doesn’t require any human attention for maintenance. Its reliability, however, depends on internet connectivity and service provider availability. In terms of backup, service providers ensure that the data being backed up to the cloud is protected via advanced encryption techniques before, after, and during transit. Plus, backed-up data can be replicated over multiple data centers, allowing for an additional layer of security.
• Hybrid cloud storage is best when you need a “seasonal” scale when facing short periods of extreme loads of data, but you don’t want to compromise the security offered by a private cloud. In terms of backup, this is probably the most cost-effective and efficient solution, as you can back up sensitive and/or frequently used data on a private cloud, and the rest on a public one.

Cloud consultants can assist you to choose the cloud backup that is best for your needs.

What Is the Company’s Maintenance and Training Policy?

The best data analytics companies will follow the most effective maintenance protocols to minimize the number of failures that might occur during production. Some companies offer predictive maintenance, which is required for the major components whose failure would cause a function loss and safety risk. Others will offer preventive maintenance, where the components of equipment items are replaced at a predefined interval. There’s no right or wrong here, and it’s not unusual for businesses to adopt a mix of the two types. 

Furthermore, some of the big data companies listed here offer in-house training. This can enable you to learn how to solve certain big data problems, as well as gain the skills to store, process, and analyze large amounts of data.