How To Define a Small Business Vs. a High-Growth Startup

How To Define a Small Business Vs. a High-Growth Startup


How To Define a Small Business Vs. a High-Growth Startup


    Difference between a Small Business and a High-Growth Startup

A startup business differs from a small business in one primary aspect: Growth.

A startup company, also referred to as a high-growth startup, is a company with a business model that is designed to be repeatable and scalable. This is directly opposed to a small business which is typically more of a lifestyle business that is not primarily concerned with scalability, but aims to sustain a particular level of income to enjoy a particular lifestyle.

What is Scalability


To better illustrate the difference between a small business and a high growth startup let’s define the term scalability.

According to, scalability is defined as, “A characteristic of a system, model, or function that describes its capability to cope and perform under an increased or expanding workload. A system that scales well will be able to maintain or even increase its level of performance or efficiency when tested by larger operational demands.”

Examples of Scalability

An example of a small business – i.e. a business that does not easily reach scale – is an auto repair shop.

Any particular auto repair shop can only reach a certain capacity of workflow given the size of the location and the number of employees. In order to continue growing, the company will need to expand to a second location, purchase duplicate equipment, hire new staff and new managers, and market the business. While this growth is achievable, it is not an operation that is easily scaled.

An example of a high-growth company – i.e. a business with high scalability – is a consumer electronics company. It may be a costly endeavor to create a prototype for a consumer electronic device, obtain intellectual property, and secure manufacturing and distribution.

However, once these obstacles are in
place, the company can quickly and easily grow without very many obstacles other than operating capital. In fact, as the company continues to scale, the operation is streamlined with economies of scale, such as the cost per unit reducing as the unit production size increases.

    • What About



Another key difference between small businesses and high-growth startups is how the two think about funding, as they typically have different sources of capital available to them.

High-growth startups typically rely on several sources of capital at different stages of the startup process. Early on, the
startup relies on friends and family funding followed by angel investors and venture capital firms.

However, small businesses typically don’t have access to the angels and VCs and therefore rely on friends and family money, bank loans, and grants.

The primary reason that angels and VCs don’t get involved with small businesses has to do with the issues of scalability, outlined above, as well as a lack of
a potential return on their investment.

    Return on Investment

For the most part, small businesses do not make good investment opportunities for angels and VCs for two reasons:

1) their lack of scalability limit the potential for a significant return, and

2) most owners of small businesses are seeking a lifestyle business and not necessarily one that they plan to sell in the
short to medium future. In order for angels or VCs to partner with a company, the business must have an exit strategy that creates a liquefiable event in which the investor gets their capital returned to them along with a return on that investment.

    Should I Launch a Small Business or a High-Growth Startup

Some questions to ask yourself when determining if a small business or a high-growth startup is right for you include:

1) Why are you starting this business? Are you looking to work hard, grow a business, and sell it? Or do you want to start a business that supports a certain lifestyle?

2) Does your product or service have a huge market?

3) Will the success of your business require outside expertise or guidance, or do you have the knowledge and experience to make this business a success?

4) Is your product thoroughly differentiated from other products on the market?

5) How much money do I need to get started?

6) Is this business scalable?

Overall, there’s no right or wrong answer. Sure, high-growth technology companies are certainly the trend now, but starting a small business will always hold it’s appeal.

The Startup Garage applauds the Entrepreneurial spirit in all of you!

Revolut Review

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Revolut Review—The Best Way To Exchange Currencies

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In the end, do what’s best for you. But if you plan on taking an international trip this year—I suggest you at least give Revolut a try.

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Hive VS HUE – Top 6 Useful Comparisons To Learn

Big Data in simple terms is a combination of structured and unstructured business data. Big Data deals with current day to day transactional data of the business, which is very complex in nature. Big Data is named one among the finest artificial intelligence tools around the global market, since its inception. However, Big Data had its own limitations in terms of storage, size, analysis, searching, sharing and presentation of data to business users.

A traditional enterprise approach that consists of a server, database and user was launched by end users. But, the database server had a bottleneck of processing huge chunks of data, under a single processor. To overcome this limitation, Google has introduced a Map Reduce Algorithm, which can process the data among a set of distributed systems. This algorithm and Big Data were later transformed into an Open Source Java framework called Hadoop by Doug Cutting and his Team. Hadoop is distributed by multiple vendors across the globe, depending on their business needs. This article intends to shed some light on Big Data technologies namely Hive and Hue.

Most of the operations in the Hadoop ecosystem are operated through command line interface but there wasn’t any user interface designed during the initial releases of Hadoop. Hue is a web user interface that performs some of the common activities with Hadoop ecosystem or Hadoop based frameworks. Hue was launched and developed by an open source Hadoop framework called Cloudera.

Hive was launched by Facebook, during the initial stages of development and later it was taken over by Apache Software Foundation. This Apache project on Hive has embedded it into the Hadoop Ecosystem. Hive was designed to interact with data stored in HDFS (Hadoop Distribution File System). Hive is similar to SQL like query language. Hive is basically, used to query and retrieve the data from HDFS. This kind of query language using Hive is known as HiveQL or HQL.

Head to Head Comparison Between Hive vs Hue (Infographics)
Below is the Top 6 Comparision Between Hive vs HUE

Key Differences between Hive vs Hue
Hue is a web user interface that provides a number of services across the Cloudera based Hadoop framework. Some of the key features include HDFS file browser, Pig editor, Hive editor, Job browser, Hadoop shell, User admin permissions, Impala editor, Ozzie web interface and Hadoop API Access. But, Hive is an analytic SQL query language that can query or manipulate the data stored in a database. Some of the key features of Hive include Map-Reduce algorithm, OLAP (online analytical processing), creating schemas on databases, performing DML & DDL operations such as CREATE, ALTER, INSERT, SELECT, UPDATE, DELETE, DROP statements on HDFS.
Hue provides a web user interface along with the file path to browse HDFS. This web UI layout helps the users to browse the files, similar to that of an average windows user locating his files on his machine. This additional feature in Hue, also helps users manually upload or move files across different directories over web UI. Files stored on the HDFS can be accessed using file browser option on Hue. Hue can be a handy tool for users who don’t prefer UNIX command line interface. But, Hive is utilized to create schemas, databases to query the database. The DML & DDL statements in Hive (CREATE, ALTER, INSERT, SELECT, UPDATE, DELETE, DROP) helps users to analyze the data stored on HDFS as per business requirements. Hive can manually process and upload the data from Text files to tables. But it cannot move the files across different directories.
Hue provides a user interface to track down job status of the map reduce jobs. These jobs can be browsed through the job browser option on the web UI. Job status on hue is represented in the form of color coding (red, green, yellow and black). Green-Successful completed jobs, Yellow – Currently running jobs, Red – failed jobs and Black – Jobs abandoned by the user manually. But, Hive, on the other hand, utilizes Map-Reduce algorithm to process the data stored on HDFS. Hive can be operated either using command line interface or web editors like Hue. Hive is usually utilized to analyze complex unstructured data. This type of analytical operations performed using Hive are scheduled as Map Reduce jobs in Hadoop ecosystem.
Hue provides a web user interface to programming languages like Hive, which can be a handy tool for users to avoid syntax errors while executing queries. Hue also returns the result set and logs after the successful query execution. Hue also provides users to analyze the data in the form of charts (pie and bar charts). Hive editor can be accessed via query editors’ option on Hue. But, Hive without hue cannot be accessed over a web editor. Visualizations cannot be created using Hive. Hive only displays the result set at the command prompt level.
Hue allows users to create and configure file permissions on HDFS. The file permissions and user roles can be accessed via security option listed on the browser. Hue provides users to track down Ozzie workflows to process the jobs scheduled on job browser. Hue also allows users to browse and access tables and databases via metastore manager and database editors. But, Hive has secured with Kerberos 2.0 authentication along with Hadoop Cluster. The workflows scheduled using Ozzie cannot be tracked using Hive. All the data stored in the form of schemas and databases can also be viewed using HiveQL or Hive.
Hive vs Hue Comparision Table
Following is the Comparision Table between Hive and Hue are as follows

Basis of Comparison


Inventor / Invention Hive was launched by Apache Software Foundation. Hue was launched by Cloudera.
Scope/ Meaning Hive or HiveQL is an analytic query language used to process and retrieve data from a data warehouse. Hue is a Web UI that facilitates the users to interact with the Hadoop ecosystem.
Installation/ Configuration Hive can be installed or configured using command line Interface of a Hadoop Ecosystem. Hue can be installed or configured only using a web browser.
Functionality Hive uses map-reduce algorithm to process and analyze the data. Hue provides Web UI editor to access Hive and other programming languages.
Implementation Hive is Implemented and accessed using a command line interface or a web UI Interface. Hue is implemented on a web browser to access multiple programs installed on Cloudera.
Dependency Hive can be embedded across multiple Hadoop Frameworks. Hue is only available on Cloudera Based Hadoop Framework.
Conclusion – Hive vs Hue
In conclusion, we have covered the introduction, key differences and few comparisons on big data technologies Hive & Hue. We also have seen some of the similarities in Hive, which are also present in SQL query language. Hue is a one-stop web UI application that has all the services across the Hadoop big data ecosystem. Hive and Hue both can be utilized and configured in the Hadoop based frameworks depending on the end user requirements. There is a lot of information available over the web along with pre-configured Hadoop virtual machines to get a brief idea of Hive & Hue implementation. Both Hive and Hue have a key role to play in modern-day Big Data analytics.

Choosing the right estimator

Choosing the right estimator

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job.

Different estimators are better suited for different types of data and different problems.

The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data.

Click on any estimator in the chart below to see its documentation.

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