Advantages of Apache Cassandra Database

As one of the better-known NoSQL database, Apache Cassandra is fast becoming a preferred database of enterprises and SME’s alike. Its robust performance in applications needing heavy write systems traversing massive volumes of data is what makes it stand apart from its contemporaries.

A typical Cassandra database consists of keyspace (similar to a schema in a relational DBMS), column families (consistent with a table in a relational DBMS), and rows/columns. It also utilizes a Cassandra Query Language (just like SQL) to retrieve records, carry out actions, and communicate with the Cassandra database.

Here are five reasons why Cassandra makes for a great database system

1. No single point of failure

Its masterless architecture makes Cassandra highly fault tolerant. Because of this, any downtime affecting a few nodes will not impact the overall performance of the system.  This enhanced fault tolerance level is a great draw for enterprises who wish to provide ‘always-on’ online services to their customers.

If we look beyond a single datacenter then Cassandra can be of great help. It allows seamless replication of the data center, thus facilitating a strong disaster recovery and backup/retrieval system within your organization.

2. Handling massive datasets made easy

Hulu, NetFlix, Instagram, and Apple, the list of enterprise users who benefit from Cassandra speak a lot about its capability to handle humongous volumes and variety of data. If your organization too faces a probability of data volume expanding exponentially and scaling up at a rapid rate then you need not look beyond Cassandra.

You can rely on Cassandra to continue delivering optimized performance without any impact of the huge rapid change in the data it is handling

3. Logging is simplified

In today’s homogenous environment, a typical company has to deal with multiple clients and servers (Android, web, iOS to name a few). In such an environment, logging and analyzing logs become huge challenges to deal with. Cassandra comes across as a viable solution to centralized logging.

This way, your development team need not spend a lot of time on logging and can instead focus on better product development.

4. Fast reads and superfast writes

Workloads like metrics collection and logging need extremely fast writes for optimal performance. This is where Cassandra scores heavily over its peers. It offers a scalable read-write performance. This means that if you know a single server’s write performance, you can accurately assess how many servers are needed in a particular cluster to meet the performance expectations.

5. Active community support

A lot of young minds are focused on expanding the possibilities around Cassandra. They are highly active and provide assistance in case of issues around managing or configuring complex database setups using Cassandra. The monitoring and troubleshooting systems around the software make it a truly high-performance open source NoSQL database system.

Thus, it is clear that Cassandra offers a host of benefits that can add tremendous business value to your tech offerings. It is time that you explore Apache Cassandra for your enterprise database management needs.

Big Data in Retail Industry

The amount of data involved is huge in the retail industry. This is the reason why industry leaders keep on looking for innovative methods to make their brands more successful.

“Big data” is the name for a collection of huge amount of data. However, it is also associated with the management, storage and use of data. With more than 6 billion mobile users all over the world, big data offers retailers the ability to understand consumer behaviour and market their brands like never before.

Here are the ways big data can help retail brands getting success in their market.

1. Getting precise insights about the retail sector

With the ever-changing market, retail brands need high-quality insights regarding the brand categories, customers, technologies and other aspects. And big data technologies prove to be a perfect choice in order to bring precision in that. The brand managers can get multiple combinations of reports that weren’t possible without big data technology.

2. Accelerating the analysis process

The ability to take action at the right moment defines the success rate in the retail sector. The brands need high-performing analytics to predict and take actions. This has become feasible with modern big data technologies.

The big data technology offers the ability to generate daily predictions and analyze the market in no time. As a result, there is no need to wait for days and lose the opportunities to succeed in the market.

3. Better coordination between brand and IT

Before big data, the retail brands always felt that there was a gap between their business and IT strategies. However, this problem can go away for good with big data implements. The IT support can design applicable strategies for retail brands including factors. The exploration, visualization, simplicity of understanding and other benefits make it easier for retail brands to adapt to big data IT solutions.

4. Fast decision-making that improves supply chain

A retail brand has to manage and handle multiple steps of the supply chain from the inventory to the shelf. These steps require immediate decision-making capacities. But that is only possible when the business leaders have enough data available regarding the channels and segments associated with their supply chain.

Knowing which area is beneficial in terms of brand popularity makes it easy to manage the supply chain for high ROI. And that is exactly what big data implementation offers for the business.

5. Understanding and fulfilling customer demands

The whole foundation of the retail industry lies in the demands of customers. Retail brands need to understand those demands in real-time in order to stock the right products and provide them to the customers. The big data presents the opportunity to get accurate data on customer demands, which helps in forecasting the potential sales. As a result, brands can stock the necessary quantity and save themselves from wastage and loss.

Big data technologies offer all those solutions that any retail brand requires in order to succeed in the market. So, it would be a smart strategy to find such solutions and get your brand visible and successful.

How to become a Data Scientist?

Data Science is providing tremendous business value such as precision in forecasting and gaining insights on what can be beneficial for the customer. Take for instance streaming sites like YouTube etc. Data science is being used to mine data about what kind of videos attract users’ interests. This helps the site to recommend similar videos based on their specific choices and interests.

5 skills every Data Scientist should have

In today’s world, where the competition is immense, data scientists are more in demand than ever. If you are interested in entering this field and want a list of skills that you need to master then you are at the right place. We have listed 5 essential skills you need to master in order to be a good Data Scientist.

1. High-level understanding of Python, Hadoop, and SQL

As a data scientist, it is essential for you to be good at programming languages like Python, Hadoop, and SQL. The data provided to a data scientist is usually in form of large data sets and being able to understand and decode it requires good programming knowledge.

Only after you have understood the dataset can you mine data and identify peculiar characteristics and patterns. The work of a data scientist is more applied than theoretical.

2. Strong Decision-Making Prowess

A data scientist should have good intuition and decision making skills to identify the product and have a concrete knowledge of the complex system and data. When a data scientist has a good product intuition, he can generate general hypothesis on how he can improve the product, and get good insights.

It is also necessary for a data scientist to know how to define metrics of the product so that he knows what he can do to make it better.

3. Ability to work well in teams

Data science is not an easy job, and it is definitely not something one can do alone. A data scientist needs to have good teamwork spirit if at all he wants to do his job as per expected outcomes. Be it ETL specialists or data analysts, or even stakeholders form the business side, a data scientist will have to collaborate with many teams from time to time. Having a good team spirit makes workflow smoother.

4. Good Communication Skills

Even if a person is good in data science, and has obtained excellent insights, it is essential for him to know how to communicate his findings in a perfect manner. All the insights obtained from such deep-end and thorough research will be of no use if it is not communicated effectively. Different business units must be able to derive the much-needed data to make better business decisions.

5. Excellent Quantitative Analysis

A data scientist must mine data and identify peculiar characteristics and behavior from the given data. Hence, quantitative analysis becomes an essential skill for a data scientist as the datasets provided are very large. In order to efficiently identify products and their behavioral characteristics, a data scientist needs to have quantitative analysis as his expert skill.

Thus, if you want to become the most sought-after data scientist in the industry, then these are the five key skills you must possess to be good at your job.

Apache Kafka – A Scalable Messaging System

Kafka is a distributed messaging system that allows to publish-subscribe messages in a data pipeline. It is a fast and highly scalable messaging system and is most commonly used as a central messaging system and cnetralizes communication between different and large data systems.

Image reference: http://kafka.apache.org/documentation.html

Advantages of using Apache Kafka

1.) Highly Scalable:

As mentioned earlier, one of the major advantages of using Kafka is that it is highly scalable. In times of any node failure, Kafka allows for quick and automatic recovery. In a world that now deals with high volumes of real-time data, this feature makes Kafka a hands down choice for data communication and integration.

2.) Reliable and Fault – Tolerant:

Kafka helps to replicate data and also supports multiple subscribers. Thus, in case of any failure there is no fear of data crash. Kafka is a fault-tolerant messaging system,  thus making it a highly reliable pub-sub messaging system among the many others

3.) High Performance:

Kafka is super efficient at handling real-time and complex data feeds with high throughput and lesser delays. The data and stored messages can run into terabytes, yet Kafka delivers high performance and the best companion for any enterprise Hadoop infrastructure.

Popular use case scenarios for Apcahe Kafka

1.) Messaging

A message broker is used for many reasons such as separating data lines from data producers, buffer and load unprocessed images etc and Kafka works as the best messaging broker to support all these activities. Also, with the credibility of being fault-tolerant and highly scalable, Kafka is a good solution for processing large scale messages.

2.) Website Activity Tracking

The main use of Kafka was to help to track and analyze real-time feeds of complete website activity such as page views, search, publish and subscribe and any activity that user performs on the site. All these activities are stored as separate topics in the data pipeline.

Kafka is also used to track high volumes of data activities as each page view can generate multiple messages.

3.) Log Aggregation

Kafka helps to collect distributed data files and puts them all together in a central place for processing. It de-clutters the extra details and only gives log and event data that has been recorded. Kafka is better suited from other log-centric systems because of greater performance and durability due to data replication.

4.) Stream Processing

Kafka helps to process data in multiple stages where the raw input data procured from Kafka topics is aggregated, enriched and transformed into new topics for further data mining.

Right from crawling content, to publishing it and further categorizing it under relevant topic and then attempting to recommend the content to users, Kafka does it all! The processing pipeline is quick and has low latency. It also provides real-time data graphs and hence is considered to be the most reliable stream processing tool.

Thus, Kafka is an amazing big data processing tool that most MNCs such as LinkedIn, Twitter, Pinterest and many more use as their publish-subscribe messaging system and also to track data. Its durability and scalability give Kafka an edge over other big data solutions.

Big Data in Business Today

With 2.5 quintillion of data produced every day, Big Data is gaining tremendous traction in analyzing this humongous volume of data from disparate structured and unstructured sources. It has proven to drive enhanced business performance with the help of real time analytics and enable smart decision making backed by strong data analysis.

Here are some examples of how our daily lives are changed for the better with the help of Big Data applications:

1 – Big Data in healthcare

As healthcare costs keeps rising, Big Data and related technologies are stepping in for more efficient patient care. It can reduce clinic visits with the help of vitals monitoring devices fitted on the patient. These monitors relay information such as heart rate or BP level, so that the physician can take real time action to prevent escalation of deteriorating health condition. This remote physician care can be administered even if the physician is located miles away from the patient.

2 – Big Data in e-commerce

An online shopper’s entire journey from visit to sale is tracked by Big Data to provide insightful information about buying behavior, preferences, and socio-economic demographic profiling. This helps in segmenting the shoppers into appropriate groups so that deeply customized and targeted marketing messages can be relayed to the appropriate group at the right time.

Big Data also tracks purchases made 3 to 4 months prior to a big ticket event like Black Friday or Big Billion Day Sale. This helps in a better understanding of what products need to be stocked and thus ensure proper inventory management.

3 – Big Data in Navigation Assistance

Our Maps services are enabled by GPS. This in turn is made more precise with the help of tons of reports fed into the systems so that the coordinates are made as accurate as possible. Big Data relies on diverse sources of information such as individual data from apps, incident reports, or road traffic reports for value added services like depicting the shortest route or the fastest route from the source to destination.

4 – Big Data in Entertainment

See recommendations in music based on your music playing preferences? This is nothing but Big Data at play. Apps like Pandora and Spotify have mastered the use of Big Data to generate personalized recommendations to music lovers based on their listening preferences. Spotify’s work in this sector is particularly interesting. It has acquired The Echo Nest to power up their music recommendation engine algorithm. The algorithm isn’t limited to simply analyzing and classifying music. It also uses web crawling to obtain information about the artist, the song, or the music label and includes this insight into the analytics engine.

To conclude

These real life use cases aptly denote the extent to which Big Data has entered our lives. From healthcare to education, there is no sector is left untouched by Big Data. As a smart business owner, it will be unwise to ignore these trends.

Why invest in Machine Learning

From automated solutions to consumer feedback systems, what is the one thing that integrated business technologies use to improve operations? What has emerged as a new age approach to understand clients better, enhance sales and purchase cycle, interpret clients’ pain points, or create consumer-centric products and services?

Yes, it is the use of machine learning and Big Data analytics.

Using machine learning to boost the efficacy of Big Data analytics is what will drive modern businesses into the next decade of growth and sustainability.

How it all began?

The sales and purchase cycle involves customers at every level. While making purchase decisions, consumers take quite a few aspects into account. Let’s take a look at these crucial factors:

• Recognizing needs: That’s the first thing every consumer needs to do. Recognizing the purchase needs is critical to finding the right products. Therefore, every customer needs to identify his buying needs and objectives.
• Figuring-out solutions: Once buyers are aware of their needs, they can make the right decisions. Proper identification of the problem will lead to effective solutions.
• Decision-making process: After figuring out the solutions, consumers will have the opportunity to take effective and successful decisions.

Machine learning and Big Data analytics play a vital role in this context.

Here’s what you need to know about the benefits of machine learning in understanding customer behaviors.

1. Machine learning decodes consumer behavior

Targeted and proper understanding of customers depends on studying their behaviors. Crucial insights into their behavior and actions will help you identify their preferences and choices. Machine learning and big data help you gain insights into consumer behavior in real-time. If you wish to accelerate your bottom line, it will be imperative to leverage the benefits of machine learning and big data analytics.

2. Matching products with consumer preferences

Accurate algorithms are an integral part of the machine-learning technology. These algorithms play a crucial role when it comes to determining the price points. Retailers will have the chance to determine price points and product availability, thus matching the right set of products with specific consumer choices.

Some of the leading brands are making the most of this technology and helping consumers make better purchase decisions. Machine learning reduces possibilities of getting confused while shopping, thus ensuring an unparalleled and satisfying experience.

3. Consumers can fulfill their demands

Machine learning coupled with big data analytics can present a crystal clear picture of consumer behavior. You know your consumers’ purchase behaviors in the past, which helps you anticipate their buying preferences.

With the machine learning technology, you can gather real-time customer information. From identifying the links clicked by them to finding out contents they choose for social media sharing, machine learning helps you comprehend customer behavior in details.

Parting thoughts

In an age where consumers are the most crucial parts of the sales cycle, personalized shopping experiences will be the key to promoting your brand. By helping you understand consumer behavior, the machine learning technology will take your brand to unsurpassed success.