Any analytics requires a certain set of data to carry out the process, and this contributed to the origination of data flow. Data flow is the process of transporting a certain set of data from one node to the other to carry out the desired analytical work. The data flow faces several hurdles in transacting the data set where it may get prone to several disabilities like theft, remodelling or bottle-neck latency, which seriously leads to several setbacks. Data pipelining plays the role of saviour here by eliminating the unnecessary manual processing steps that pave the way to uninterrupted dataflow.
A primary data storage system that employs namenode and data node architecture to contrivance distributed file system to achieve high-performance data across Hadoop clusters. HDFS highly supports faster data processing between any two nodes, and it makes it happen with the help of programmatic data processing framework named MapReduce. HDFS is known for its large scale implementation, low-cost commodity support that helps to deliver several beneficiaries when it comes to data pipelining.
Kafka is a community distributed event streaming platform which has the potency of handling several trillion events in a single day. Kafka has registered a significant growth since its inception from message queuing to event streaming platform.
The implementation of Kafka witnessed in diversified applications like creating custom web apps, web development, microservices, data monitoring and analytical services. Kafka runs on one or more servers that span multiple data centres, and it stores a stream of records according to the category which is known as “topics”.
A high-speed and in-memory data processing engine coupled with delicate and poignant development API helps data analysts to carry out streaming, machine learning and SQL workloads that demand faster access to the dataset. Running the spark over Hadoop yarn will help the developers to create application anywhere by completely utilizing spark’s power, derive insight and enrich the data as well.
An Apache Sparks-based API, GraphX is used to carry out the graph and graph-parallel computation that unifies ETL (Extract, Transform and Load) process, exploratory analysis and iterative graph computation in a single system. GraphX holds growing collections of algorithms and builders that simplifies several analytical tasks. This Spark’s API is highly flexible as it runs in both graphical and computational methods. GraphX delivers quick processing results when compared with other graph systems by maintaining its flexibility, fault tolerance and ease of use.
A data warehouse framework developed on the Hadoop platform that handles queries and analyses data stored in HDFS, this framework is open-source software that helps programmers to analyze a large set of data using Hadoop. Even though Hadoop can handle huge data sets still, it suffers from low-level of MapReduceframework that demands custom coding process. Here Hive comes to rescue by providing SQL like declarative languages that easily addresses programming queries.
Nifi is a dataflow system developed based on the concepts of flow-based programming, that highly supports scalable graphs data routing, transformation, and system mediation logic. Nifi has an interactive web interface used to design, control, attain feedback, and monitor dataflow activities. It is found to be highly-configurable in multi-dimensional services like:
- Loss tolerant Vs Guaranteed delivery
- Low latency Vs High throughput
- Priority-based queuing
Data visualization is the concept of representing a particular data set in the pictorial or graphical format that helps analysts to take an informed decision over the key insights in different streams of industries. Data visualization heals out the complexity defect in sensing the actual information in any data format and presents it in a much easier way to carry out several organizational activities like:
By focusing on concerned area of improvement
Presents the factors influencing customer buying behaviour
Helps in providing clarity over product positioning
Estimate accurate sales volume
Tableau is the most promising data visualization tool used by the business intelligence industry to access and drive insight from highly complicated raw data with earning any technical knowledge. Tableau helps to analyse data quickly than any other visualization tool where it generates useful spreadsheets and dashboards for better understanding. Tableau has the capability of exploring data with limitless visual analytics.
Kibana is a handy data visualization platform developed using Elastic, which helps in handling high volume range of streamlined and real-time data sets in a seamless way. Kibana python based data processing platform that has several built-in third-party libraries with it, installing this computing platform has an equivalent effect to that of python that uses some common libraries like Numpy, Pandas, Scrip, and Matplotlib which makes your python installation much easier.
Data analytics involves the process of attaining the actual information hidden behind the raw data and make conclusive decisions that would yield the desired result to any organization or business. Data analytics is known to reveal the actual trends and metrics present in the raw data which would get lost either way if it not exposed to this method. Attaining pure trends and metrics will result in optimised process execution and increases the efficiency of the businesses.
KNIME is an open source software platform that helps in creating data science application and services. Being a data analytics platform, KNIME keeps constantly updating with its new development that helps to understand data science workflows in a much better way and access the reusable component for everyone.
A python based data processing platform that has several built-in third-party libraries with it, installing this computing platform has an equivalent effect to that of python that uses some common libraries like Numpy, Pandas, Scrip, and Matplotlib which makes your python installation much easier. Anaconda is the premium distribution of Python and R data science package with more than 100 new packages in it. Anaconda delivers several benefits like:
- Multiple platform installation of python
- Categorizing diversified development environments
- Dealing with incorrect privelge
- Running specified packages and libraries
An open-source, free, interactive web tool that helps developers to combine software code, derive computational output, bring in the explanatory text, and multimedia document under a single shade. This open-source web tool is programming language friendly as it has supported over 40 languages like R, Python, Scala, and Julia since its inception. It leverages big data integration by using tools like Apache Spark, and it explores the same data using Pandas, skit-learn, and TensorFlow.
Scala is a perfect combination of a functional and object-oriented language, which is highly scalable that projects it unique from other programming languages. It is tailor-made in a way to portray generic programming patterns in a more precised and type-safe way. Scala delivers multiple benefits to its community by easing up the pressure over developers in handling the code by providing easily deployable and reusable codes that comes out with a limited number of bugs.