Reading Time: 4 minutes If you’re looking to store and manage data efficiently and effectively, then MarkLogic Database is an excellent choice. It allows you to easily write CRUD operations (Create, Read, Update, Delete) within the database in order to keep your data organized and secure. In this article we’ll discuss how you can write CRUD operations with MarkLogic Database so that you make the most of your data Continue Reading
Reading Time: 3 minutes Install MarkLogic(10.0-9.4) in Ubuntu 20.04.4 : Mark-Logic Server is an Enterprise NoSQL database. It is a document-centric, transactional, search-centric, structure-aware, schema-agnostic, XQuery- and JavaScript-driven, high-performance, clustered, database server. It is an enterprise-class NoSQL database that includes a REST-enabled full-text search feature. You can use it to store, search, and query massive amounts of data and represent documents having various formats. It also exposes its core Continue Reading
Reading Time: 4 minutes If you want to build Microservices, You may prefer Axon, a Java-based framework. It provides utility to implement CQRS (Command Query Responsibility Segregation), Event Sourcing, and DDD (Domain Driven Design) architectural patterns while developing a software application. Axon facilitates the implementation of Command Handling, Event Routing, Event Sourcing, Snapshotting, and many more building blocks. It has very friendly APIs to use these implementations and build Continue Reading
Reading Time: 3 minutes NoSQL Database Couchbase works as a NoSQL database. So, we will first know about NoSQL Databases. NoSQL Databases use a variety of data models for accessing and managing data, optimized for large data volume, low latency and flexible data models. Features of NoSQL Database Flexibility: Enables faster and more iterative development. Scalability: NoSQL databases are designed to scale out by using distributed clusters. High-performance: Enable Continue Reading
Reading Time: 2 minutes In a previous blog, we had seen BigQuery facilitate efficient data warehouse schema design. BigQuery supports the nested & repeated columns. We can use a combination of ARRAY and STRUCT data types to define our schema in BigQuery. It enables to denormalize data efficiently in single table. In this blog, for the same schema of sales data, we will execute a few DML operations on nested array fields. Schema In Continue Reading
Reading Time: 3 minutes Conventional data warehouses support data models based on star schema and snowflake schema. In these models, there are a number of fact tables and dimension tables. In order to minimize redundancy it is recommends to split data into multiple tables in . This is a normalization process. Normalization is the technique of eliminating the redundant data. It minimize the insertion, deletion, and update anomalies. It saves the disk Continue Reading
Reading Time: 3 minutes We are living in the 20th century the century of technologies. Because of this, we come across a lot of data in our daily life. So it is important for us to have a database that can help in maintaining a huge amount of data. Now we have many popular databases in the market like PostgreSQL, Cassandra, MySQL MongoDB, and many more. But the question Continue Reading
Reading Time: 3 minutes Hi Folks!! In this blog, we are going to learn how we can integrate Flink with Kafka and Cassandra to build a simple streaming data pipeline. Apache Flink is a framework and distributed processing engine. it is used for stateful computations over unbounded and bounded data streams.Kafka is a scalable, high performance, low latency platform. It allows reading and writing streams of data like a messaging system.Cassandra: A distributed and wide-column Continue Reading
Reading Time: 4 minutes Morpheus brings the world of Graph Processing to Spark. It’s Property Graph Model and Cypher language allows easy inclusion of Graph into Spark pipelines
Reading Time: 3 minutes Today, everyone is talking about Delta Lake. Why? Ever tried to find the answer to this question? Yes or No doesn’t matter, don’t worry here in Part1 we will be discussing the same & also will be targetting the following questions: What are the features missing from Apache Spark? What kind of issues it causes in executing Data Lake? Answering the above questions will definitely Continue Reading
Reading Time: 5 minutes While transitioning from a Relational to a NoSQL Database, architects expect none or a minimal effect on performance with the scaling up of the size of data. Dealing with a huge amount of data may be the USP of a Database, but still, we need to design things in order to make them run well at scale. In this blog, I’d try to explain what Continue Reading
Reading Time: 2 minutes Apache Spark is quickly adopting the Real-world and most of the companies like Uber are using it in their production. Spark is gaining its popularity in the market as it also provides you with the feature of developing Streaming Applications and doing Machine Learning, which helps companies get better results in their production along with proper analysis using Spark. Although companies are using Spark in Continue Reading
Reading Time: 3 minutes Spark doesn’t provide some of the most essential features of a reliable data processing system such as Atomic APIs and ACID transactions as discussed in the blog Spark: ACID compliant or not. Spark welcomes a solution to the problem by working with Delta Lake. Delta Lake plays an intermediary service between Apache Spark and the storage system. Instead of directly interacting with the storage layer, Continue Reading
