![]() TL DR: Apache Cassandra is a heavyweight MongoDB alternative that’s ideal for enterprises with large datasets. However, some users report that high availability is far from consistent and that performance can be unpredictable. As every node in the cluster is separate, you don’t have to worry about any bottlenecks in the network. Initially developed for Facebook, you can deploy Apache Cassandra across multiple servers pretty quickly. ![]() In this case, data is replicated to multiple nodes to create a fault-tolerant system. This open-source NoSQL database enables operational simplicity and provides support for replication across multiple data centers and cloud availability zones. If you want high availability and scalability while ensuring performance, Apache Cassandra is the answer. So, we went to work, dug through the data, and came up with a list of the top 11 MongoDB alternatives (both SQL and NoSQL and free and paid options) that developers and data engineers should know about. When it comes to NoSQL databases, the real difficulty is knowing which database is suitable to solve your specific problems. ![]() However, with databases like MongoDB, data consumption tends to be high due to denormalization. Furthermore, developers are moving away from MongoDB because of different issues with the data management software. It’s also essential because modern applications need much more than a single database to function properly. In this scenario, the architecture uses collections and documents instead of tables and rows. NoSQL is a non-relational database technology (which is primarily schema-less) that can be used in massive data applications where data is unstructured. At the time, none of the databases available could scale enough to meet the demands of the marketplace. NoSQL databases emerged in 2009 as popular websites started scaling up. MongoDB has traditionally been popular among JavaScript developers, but that’s slowly changing. It supports various forms of data and is probably the most famous NoSQL database. Scientific workflows can be understood as arrangements of managed activities executed by different processing entities.MongoDB is a document-oriented database management system. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems.Ĭloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM).
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