Big Data Technologies

Big Data

A software tool that analyzes, processes and interprets large amounts of structured and unstructured data that cannot be processed manually or traditionally is called Big Data Technology. This helps to draw conclusions and predict the future in order to avoid many risks. The types of Big Data technologies are operational and analytical. Operational technology deals with daily activities such as online transactions, social media interactions, etc., while analytical technology deals with the stock market, weather forecasting, scientific computing, etc. There are excellent data technologies in data storage and retrieval, concept and analysis.

Big Data Technologies

To keep you informed of upcoming trends and technologies, here is a list of some of the Big Data technologies with a great explanation.

Apache spark

It is a fast data processing engine. It is designed with real-time data processing in mind. Its large machine learning library works well in place of AI and ML. It processes data on parallel and clustered computers. The basic data type used by Spark is RDD (Flexible Distributed Data Set).

NoSQL database

It is a spam database that provides instant storage and retrieval of data. The ability to handle all types of data, such as structured, semi-structured, unstructured and multidimensional data, is unique.

There are no MySQL databases in the following categories.

• Document database: it stores data in the form of documents which can contain many pairs of different key values.
• Graph stores: it stores data which is generally stored in a network format such as social media data.
• Key value stores: these are the simplest NoSQL databases. Each element of the database is stored, with its value, as an adjective (or "key").
• Extended Column Storage: This database stores data in a column format instead of a single row format. Cassandra and H. Base are excellent examples.

Apache Kafka

Kafka is a distributed event streaming platform that manages a wide range of events every day. Because it is fast and scalable, it creates real-time streaming data pipelines that reliably retrieve data between systems or applications.

Apache Ozzy

It is a workflow planner system to manage Hudop tasks. These workflow works are defined in the form of a dynamic ecclesiastical graph (DAG).

Apache Airflow

It is a platform that monitors and monitors the workflow. Intelligent planning allows you to manage the project efficiently. In case of failure, Airflow DAG has the possibility to re-run the instance. Its comprehensive user interface makes it easy to view pipelines operating at different stages, such as production, monitoring and troubleshooting when needed.

Apache beam

It is a uniform model for defining and implementing data processing pipelines, including ETL and continuous sequencing. The Apache Beam framework provides a summary between the logic of your application and the Big Data ecosystem, since there is no API that binds to all frameworks such as HUDPO, Spark, etc.

ELK Battery

ELK is known for Elasticsearch, Logstash and Kibana.

Elastic Search is less than a schema database (indicating each field) which has powerful search capabilities and is easily scalable.

LogStash is an ETL tool that allows us to create, modify and store events.

Cabana is a dashboard tool for Elasticsearch, where you can analyze all of the stored data. The practical knowledge learned from the firm helps to develop a strategy for an organization. From capturing changes to forecasting, Cabana has always been very helpful.

Dockers and cabernets

These are emerging technologies that support applications running on Linux containers. Docker is a collection of open source tools that help you build, ship and operate any application anywhere.

Cubernets is also an open source container / orchestration platform, which allows a large number of containers to work together seamlessly. This ultimately reduces the operational load.

Tensorflow

It is an open source machine learning library used to design, create and train deep learning models. All calculations are performed in tensorflow with data flow graph. Graphics include nodes and edges. Nodes represent mathematical operations, while edges represent statistics.

Tensorflow is useful for research and preparation. It was designed keeping in mind that it can run on multiple processors or GPUs and even mobile operating systems. It can be implemented in Atma, C ++, R and Java.

Presto

Presto is an open source MySQL engine developed by Facebook, capable of handling petabytes of data. Unlike Hive, Presto does not rely on MapReduce technique and is therefore faster in data recovery. Its architecture and interface are fairly easy to interact with other file systems.

Due to the low delay and easy interactive queries, it is becoming more and more popular for big data management these days.

Poly base

Polybase works on top of SQL Server to access secure data in PDW (parallel data warehouse). PDW is designed to process any relevant data volume and offers integration with HUDPO.

Hive

Hive is a platform used to query and analyze data on a large scale. It provides a SQL type query language called HiveQL, which is internally converted to MapReduce and then processed.

With the rapid growth of data and the organization's tremendous effort to analyze big data, technology has introduced many mature technologies into the market that are extremely rewarding to know. Nowadays, big data technology responds to many needs and concerns of the company, enhances operational efficiency and predicts relevant behavior. A career in big data and related technologies can open many doors of opportunity for individuals as well as companies.