Big Data Platform – On the previous occasion, we have discussed Big Data and Data Science. Which Big Data is a special term used for data that exceeds the processing capacity of conventional databases because the amount is too large, moves too fast, and does not match the structural capabilities of traditional database architectures.
So we have to carry out a process with an integrated system capable of handling Big Data called Big Data Analytics. While data science is the study of raw data and unstructured data that processes it through expertise.
Use skills such as statistics and mathematics, programming or IT, data processing, data analysis and broad knowledge of various fields. To assist in dealing with this, we need supporting tools, both tools for big data and data science.
Therefore, on this occasion we will discuss Tools for Big Data Analytics Platforms and Data Scientists. Let’s look at the following article to find out more.
Tools for Big Data Analytics Platforms
There are many tools that the big data analytics platform can use, namely:
1. Apache Hadoop
We can’t talk about big data without mentioning Hadoop. Hadoop is a framework that enables distributed processing of large data sets across groups of computers using a simple programming model with the MapReduce programming model.
Cloudera is a modern platform for data management and analysis that provides the Apache Hadoop platform to help businesses solve their most challenging data-related problems, especially large amounts of data.
If you want to make an analogy, Cloudera is similar to Red Hat. Both are based on Hadoop technology, but with the Cloudera distro.
3. Apache Cassandra
Quoting from medium.com, Cassandra or full APACHE CASSANDRA is an open source product for database management that Apache distributes.
So Apache Cassandra is very scalable (can measure it) and designed it to manage structured data.
Structured data with a very large capacity (Big Data) spread across many servers. Cassandra is an implementation of NoSQL (Not Only SQL) like mongoDB.
Altamira owns Lumify, which is renowned as a national security technology. Lumify is an open-source big data integration, analytics, and visualization platform.
Its main features include full-text search, visualization of 2D and 3D graphics, automatic layout, analysis of links between graph entities, and integration with mapping systems.
In addition, the main features of Lumify are geospatial analysis, multimedia analysis, and real-time collaboration across a series of projects or workspaces.
Big Data Integration products include:
- Open Studio for Big Data is great for prototyping big data pipelines.
- Big Data Platform is a platform that has a user-based subscription license. The components and connectors are MapReduce and Spark. It provides Web, email, and telephone support.
- The real-time big data platform is a user-based subscription licensed platform with components and connectors including Spark streaming, machine learning, and IoT.
Tools for Data Scientists
There are many tools that can be useful for data scientists, which are as follows:
1. Microsoft Excel
Microsoft Excel is a data processing application that uses spreadsheets for data and command management. We cannot deny, almost all companies use Microsoft Excel to process data. In terms of data science, Microsoft Excel participates to make small-scale data analysis easier.
Then there are several features that Ms Excel provides such as pivot tables, add-ins, teams, and macros. These features are very useful features for conducting data analysis.
In addition, there are many supporting formulas such as financial, statistical, and engineering that can make it easier to perform calculations on data using certain methods.
2. SAS (Statistical Analysis System)
Quoting from advernesia.com, SAS is software that most countries have used to perform statistical analysis and financial planning.
Indonesia is one of the countries that has become a loyal customer and partner of SAS, especially the Directorate General of Treasury of the Ministry of Finance of the Republic of Indonesia.
SAS is the best choice for big data analysis because the management of hardware resources such as processor and RAM is very efficient.
So that in 2015, SAS was ranked first in the “Magic Quadrant” in terms of computational modeling and data mining execution.
In addition, SAS has compatibility with relatively younger big data software, such as Hadoop, Pig and Hive. However, in terms of price, SAS software is still the most expensive software in the data analysis software range.
3. Apache Spark
Apache Spark is a super-fast integrated analysis engine (software) for processing data on a large scale; includes Big Data and machine learning. In addition, Apache Spark has a different algorithm than Map/Reduce, but can run on top of Hadoop via YARN.
In more detail, Apache Spark can be defined as an engine for processing large-scale data in-memory, complete with an elegant and expressive development API.
This is to make it easier for data workers to execute jobs that require fast repeated access to the data being processed. Like streaming, machine learning, and SQL, efficiently.
So that the core of Spark is a distributed execution engine, and Java, Scala and Python APIs are provided as platforms for developing distributed ETL (Extract, Transform, Load) applications.
Then, additional software libraries, which build on top of the core, facilitate various types of work related to streaming, SQL, and machine learning.
Tableau is an excellent data visualization tool that was recently acquired by Salesforce, one of the world’s leading enterprise CRMs. Focused on providing a clear representation of data in a short amount of time, Tableau can help with faster decision making.
But it does so by leveraging online analytical processing cubes, cloud databases, spreadsheets, and relational databases.
After reading the explanations in this article, maybe you are interested in learning one of them. Thus a short article about Tools for Big Data Analytics Platforms and Data Scientists. Hope it is useful.
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