At Cazton, we have Enterprise Search experts who have years of experience working with many different enterpise search technologies including but not limited to Apache Solr, Lucidworks Fusion, ElasticSearch, ELK Stack. Our expert team of Architects, Consultants and Developers can build custom applications and consult you with any scalability issues, explain hardware requirements and offer best practices and architectural design patterns that suit your application requirements. Our experts have worked on projects with terabytes of data that are scaled across several clusters and helped many customers develop Solr/ElasticSearch integrated applications that offer great performance, high scalability, availability and fault tolerance.
How do you decide which search engine to pick? Do you look which languages it supports, compatibility with devices or the ease of use? How does a search engine work? Can a search engine be more than a full-text search? Would you like to get more information and analytics out of your mammoth size data?
Search is one of the most important aspects in any application. Could you imagine Amazon, one of the world's largest e-commerce websites, without a search functionality? It would be nearly impossible to find products if we had to look for them manually. A robust search in this case is the key towards best user experience, increased customer traffic, growth in sales and generation of customer data, which is invaluable for such a big organization.
Search is one of the most important tools in any web application. Having a robust and fool-proof search system can boost your business growth in many ways. One such technology that empowers search is called Elasticsearch. Unlike traditional RDBMS, Elasticsearch is designed to allow full-text searching. It allows you to create your own search-engine that is fast, powerful and scalable. In addition to web searches, Elasticsearch is also used for log analysis and big data analytics.
Do you have a good caching strategy for your applications? Have you felt the pain of sticky sessions? Have you had a caching strategy that didn't work for you? Do you need a caching strategy that scales seamlessly with the least amount of effort? If the answer to any of these questions is a "yes," the good news is that you are in the right place.
Database technologies have undergone several generations of evolution, right from flat-file systems to relational databases to schemaless databases. Some people might say that traditional relational databases are a thing of the past, but that is not true for all the scenarios. Changing requirements and evolution of the internet has meant that new types of databases have emerged, but most have specific use cases, which makes it difficult to decide on which database should be used when. At the same time, different types of data models have emerged throughout the history of databases but only Relational and NoSQL models have prevailed.
Did you know PostGres is the fastest growing relational database that is not only free and open source, but rivals the performance of paid RDBMS databases like Oracle and SQL Server? It is no surprise that PostGres has consistently ranked as one of the top four relational databases by multiple credible research studies comparing database engines.
Did you know relational databases can scale up, but have a hard time scaling out? NoSQL databases, on the other hand, are meant to scale out with commodity-grade hardware. Many organizations prefer using NoSQL over SQL databases as it offers a great set of features. There are different types of NoSQL databases available including Key-Value Store, Document Databases, Column-Family Databases, Graph Databases and Full-Text Search Engine Databases.
The evolution of database technologies has been exceptional. Right from the first pre-stage flat-file systems to relational and object-relational databases to NoSQL databases, database technology has gone through several generations and its history has spread over more than 50 years now. A time has come when database technology has taken the next step forward to become more scalable, globally distributed and multi-model. Welcome to the new world of Cosmos DB!
Did you know there are more than 1500 companies using Cassandra to handle huge volumes of data? Did you know that some of the largest production deployments include Apple's, with over 75,000 nodes storing over 10 PB of data, Netflix (2,500 nodes, 420 TB, over 1 trillion requests per day), Chinese search engine Easou (270 nodes, 300 TB, over 800 million requests per day), and eBay (over 100 nodes, 250 TB)?
Spark is an open-source, lightning fast, cluster computing framework that provides a fast and powerful engine for large-scale data (Big Data) processing. It runs programs up to 100x faster in-memory and 10x faster on disk when compared to Hadoop’s MapReduce system. The reason for Spark’s success is its ability to process data in-memory (using RAM) that allows faster retrieval of data as compared to querying and searching on disk.
Over the years, Spark has seen great acceptance in the technology industry. When it comes to large scale data processing or Big Data analytics, Spark has gained a lot of attention due to its lightning fast processing speed, batch and stream data processing, support for a variety of data sources and easy to integrate with applications written in C#, Java, Scala, Python and R.
With every passing second, the amount of data shared and transferred between humans is unimaginable. To manage, analyze, make predictions and decisions using that data is a daunting task. With data being a critical asset, companies today strive to understand the latest market trends, customer preferences and other requirements, thus making understanding large amount of data imperative.
Imagine a process which converts unstructured, unreadable pieces of information into something that is extremely valuable for your organization? information that gives you insights about your business, your products, customers and their preferences. Now imagine getting those insights in real time! We are talking about a process that gives you instant information about an active transaction. Such information is always valuable, isn't it