Enterprises often have a need to work with data stored in different places; because of the variety of data being produced and stored, it is almost impossible to use SQL to query all these data sources. These two things represent a great challenge for the data science and BI community. Prior to working on the […]
Modern businesses generate, store, and use huge amounts of data. Often, the data is stored in different data sources. Moreover, many data users are comfortable to interact with data using SQL while many data sources don’t support SQL. For example, you may have data inside a data lake or NoSQL database like MongoDB, or even […]
Forecasting air quality is a worthwhile investment on many different levels, not only to individuals but also communities in general, having an idea of what the quality of air will be at a certain point in time allows people to plan ahead, and as a result decreases the effects on health and costs associated with […]
New technologies, communication systems, and information processing algorithms demand data rates, availability, and performance targets. Accordingly, the data processing procedures implemented with data (messages) calls for technologies capable of handling this high demand. One of these technologies is RabbitMQ – which is used to develop service-oriented architecture services (SOA) and distributed resource-intensive operations. However, it […]
Data analysis and data visualization are essential components of data science. Actually, before the machine learning era, all data science was about the interpretation and visualization of data with different tools and making conclusions about the nature of data. Nowadays, these tasks are still present. They just became one of many miscellaneous data science jobs. […]
In datasets, very often some records do not match with the rest of the data by error or by nature. These kinds of records are useless and even harmful to ML models. In other problems, the sole purpose is to detect anomalies. For example, in health-monitoring systems in hospitals or credit fraud detection. Either way, […]
In the last few years, more and more companies have realized the value of data. Therefore, the popularity of data analytics has been growing rapidly. In general, data analysis can be performed in several ways, which are classified into subtypes depending on the analysis task: descriptive, exploratory, inferential, predictive, causal, and mechanistic. Each of these […]
Amazon Simple Storage Service (S3) is an object storage service that offers high availability and reliability, easy scaling, security, and performance. Many companies all around the world use Amazon S3 to store and protect their data. PostgreSQL is an open-source object-relational database system. In addition to many useful features, PostgreSQL is highly extensible, and this […]
Topic modeling is one of the most widespread tasks in natural language processing (NLP). This is one of the vivid examples of unsupervised learning. The main goal of this task is the following: a machine learning model should be trained on the corpus of texts with no predefined labels. In other words, we don’t have […]
Nowadays, relevant analysis of different data is an important stage of business and technical research and development. Often the data is received in the form of serial info messages (queues). This is typical for data loggers and recorders, IoT developments, live-tracking systems, communication and navigation systems, etc. After that, the following information is sent to […]
Data in its natural form is not that valuable if you cannot visualize it. There are lots of visualization libraries available in the community, which may make it difficult to select one. In this tutorial, we hope to make your selection process a little bit easier by showing you how to work with Dash. Dash […]
Azure Data Lake Storage Gen2 is a new version of the storage solution available on Azure cloud platform. It mixes the best features of both Azure Data Lake Storage Gen1 and Azure Storage. Azure Data Lake Storage Gen1 supports hierarchical file system for storing data in directories. It allows implementing some features for both file […]
HDFS is a well-known distributed file system based on the Apache Hadoop project. It allows storing large amounts of data safely in a highly performant system which can scale on demand. With the help of HDFS data, consumers can work with really big data distributed between nodes in a cluster. Dremio provides support for connecting […]
Azure Data Lake Store (ADLS) is one of the several offerings of Microsoft’s Azure cloud platform. Its primary function is to store the enterprise data, mainly big data. It is a highly scalable and secure system. Besides data storing, the Azure Data Lake has other interesting features. Some of them include: In this tutorial, we […]
Amazon Redshift is a powerful data warehouse service in the cloud. It is a simple mean of analyzing data across your data warehouse and data lake. Moreover, it is cost-effective. Redshift delivers ten times faster performance than other data warehouses because of involved technics, such as machine learning, massively parallel query execution, and columnar storage […]
