Tag: apache hive

Apache Hive Architecture – Data Warehouse System for free

Apache Hive Architecture – On the way to Industry 4.0, companies are trying to record all business processes as far as possible in order to subsequently optimize them through analysis.
Data warehouse systems provide central data management. Thus, only one data truth exists. In addition to persistence, these information systems take care of sorting, preprocessing, translation and data analysis.
If you want to know more about what a data warehouse system is, check out our article on the subject.

What is Apache Hive

Hive is a data warehousing software project and part of Apache, an open source and free web server software. Learn more about Apache here.
It is built on the Big Data framework Apache Hadoop and was released in 2010. Since then it has been continuously improved and extended by an industrious community.

Apache Hive Architecture – Built on top of Hadoop

The query language used by Hive, called HiveQL, is SQL based and allows querying, aggregation and analysis of unstructured data. Hive does not work with the schema-on-write (SoW) approach like relational databases, but uses the so-called schema-on-read (SoR) approach.

What are the biggest advantages of Hive?

Data from relational databases is automatically converted into MapReduce or Tez or Spark jobs. Hadoopclusters are based on MapReduce, a Google programming model for concurrent computation on computer clusters, and powerful stream-based data analysis pipelines can be created with Apache Spark. This ensures full compatibility with the Apache ecosystem, which can be modularly tailored to the needs of an application.

The figure shows the main Apache Hive features
Apache Hive Features

Another advantage of Hive is that the tables are similar to the tables in a relational database. Data is queried using HiveQL. A declarative SQL-like language.
HiveQL allows multiple users to query data simultaneously. Hive supports a variety of data formats and provides a lightweight but powerful translation feature.
For data analysis, custom MapReduce processes can be written and run on clusters in parallel for high performance.

Apache Hive Architecture

Basically, the architecture of Hive can be divided into three core areas. Hive communicates with other applications via the client area. The integration is then executed via the service area. In the last layer, Hive stores the metadata, for example, or computes the data via Hadoop.

The figure shows the basic three-part core architecture of Apache Hive.
Apache Hive Architecture

Hive Clients

Apache Hive can be accessed via different clients. In addition to Open Database Connectivity (ODBC), an SQL-based application programming interface (API) created by Microsoft, there is Java Database Connectivity (JDBC), an SQL-based API developed by Sun Microsystems to allow Java applications to use SQL for database access. Hive also provides a high-performance Apache Thrift connection.

Hive Services

The core and central control of the Hive Services is the so-called driver. This
receives HiveQL commands and is responsible for their execution against the Hadoop system. It typically consists of a compiler that translates HiveQL requests into abstract syntax and executable tasks, an optimizer that aggregates, splits, and optimizes for better performance and scalability, and an executor that interacts with Hadoop’s job tracker and passes tasks to the system for execution.

Apache Hive also provides the ability to submit these tasks directly to the driver. Using the Command Line and User Interface (CLI + UI), it is possible to directly influence the process.

Metadata about persistent relational entities, i.e. databases, tables, columns and partitions are managed by the metastore.

Hive Storage and Computer

The metadata is stored here in a persistence. The results of the query and the data loaded into the tables are stored on HDFS in the Hadoop cluster.

Apache Mahout – A Powerful Open Source Machine Learning Project

Apache Mahout is a powerful machine learning tool that comes with a seamless compatibility to the strong big data management frameworks from the Apache universe. In this article, we will explain the functionalities and show you the possibilities that the Apache environment offers.

What is Machine Learning?

Machine learning algorithms provide lots of tools for analyzing large unknown data sets.
The art of data science is to extract the maximum amount of information depending on the data set by using the right method. Are there patterns in the high-dimensional data relationships, and how can they be represented in a low-dimensional way without much loss of information?

scikitLearn ml
Fields of machine learning

There is often a similar amount of information in the failure as when an algorithm was able to successfully create groupings.
It is important to understand the mathematical approaches behind the tools in order to draw conclusions about why an algorithm did not work.
If you don’t know the basic machine learning categories, it’s best to read our article on the subject first.

Machine Learning and Linear Algebra

Most machine learning methods are based on linear algebra.
This mathematical subfield deals with linear transformations, vector spaces and linear mappings between them.
The knowledge of the regularities is the key to the correct understanding of machine learning algorithms.

What is Apache Mahout

Apache Mahout is an open source machine learning project that builds implementations of scalable machine learning algorithms with a focus on linear algebra. If you’re not sure what Apache is, check out this article. Here we introduce you to the project and its main projects once.

Mahout was already released in 2009 and since then it is constantly extended and kept up-to-date by a very active community.
Originally, it contained scalable algorithms closely related to Apache Hadoop and MapReduce.
However, Mahout has since evolved into a backend independent environment. That is, it operates on non-Hadoop clusters or single nodes.


The math library is based on Scala and provides an R-like Domain Specific Language (DSL). Mahout is usable for Big Data applications and statistical computing. The figure below lists all machine learning algorithms currently offered by Mahout.

The figure below lists all machine learning algorithms currently offered by Apache Mahout.
Implemented mathematical functions and algorithms

The algorithms are scalable and cover both supervised and unsupervised machine learning methods, such as clustering algorithms.

Apache Mahout covers a large part of the usual machine learning tools. This means that data can be analyzed without having to change frameworks. This is a big plus for maintaining compatibility in the application.

Apache Ecosystem

The framework integrates seamlessly into the Apache Ecosystem. This means that an application can access the entire power of the data processing platforms and build very high-performance big data pipelines. The following figure shows the Apache data management ecosystem.

Apache Mahout ecosystem
Apache Mahout ecosystem

Through connectivity to Apache Flink, stream data analysis pipelines can be built, or with Hive data from relational databases can be automatically converted into MapReduce or Tez or Spark jobs.