Big enterprises and organizations process tons of data every day through their application database. Their database should be faster enough to handle big volumes of data. Usually, developers implement traditional RDBMS to handle the application’s colossal data, but sometimes it lags in speed and performance. So to process data, quicker concepts like Apache Ignite are developed. Apache Ignite does not require users to replace their existing databases. It works on top of RDBMS, NoSQL, and Hadoop data stores.
Apache ignite is used to cache data from an underlying data source like a relational database or Hadoop/HDFS. So “Apache Ignite” responds to the request without evoking the main database. If the cache does not have the data, only then it reads the data from the underlying data source.
Apache Ignite is an open-source distributed database, caching, and processing platform designed to store and compute large volumes of data. Apache Ignite is up to 1 million times faster than traditional databases. It can be inserted seamlessly between a user’s application layer and the data layer.
( image source: gridgain)
Apache ignite is based on Grid computing. The technology utilizes the resources of many computers (commodity, on-premise, VM, etc.)
The Apache Ignite unified API supports a wide variety of standard protocols for the application layer to access data. Supported protocols include SQL, Java, C++, .Net, PHP, MapReduce, Scala, Groovy, and Node.js.
Ignite offers a distributed in-memory data store that renders in-memory speed and unlimited read and write scalability to applications. It can work both in-memory as well as on-disk and provides key-value, SQL, and processing APIs to the data. It supports any kind of data- structured, semi-structured, and unstructured. Regardless of API, the data in Ignite is stored in key-value pairs. Ignite can process terabytes of data with in-memory speed. Ignite supports SQL and ACID transactions across multiple cluster nodes. Ignite automatically controls how data is partitioned.
Apache Ignite can be deployed in cloud environments or on-premises. Though Ignite memory-centric storage works well in-memory and on-disk, the disk persistence can be disabled, and Ignite can act as a distributed in-memory database as well.
Anyone who has worked with Apache ignite has come across a variety of client connectors. With many options available, the developer is often seen confused on picking up the right connector.
When a client connects with an application database, the connection is propagated through special protocols. Ignite supports several protocols for client connectivity to Ignite clusters, including Ignite Native Clients, REST/HTTP, SSL/TLS, and Memcached SQL.
The types of Ignite client connectors include,
You can pick client connectors based on the following criteria.
RDBMS (Relational Database Management System) is always under the scanner in terms of its efficiency to handle Big Data, especially if it is unstructured data. Since the existence of both Big Data and RDBMS are evident, new technologies are developed for their peaceful co-existence.
Greenplum database is one among them.
What is the Greenplum Database?
Greenplum Database is an open-source massively parallel data server to manage large-scale analytic data warehouses and business intelligence workloads. It is built and based on PostgreSQL (RDBMS). Greenplum also carries features that are unavailable within PostgreSQL, such as parallel data loading, storage enhancements, resource management, and advanced query optimization.
Greenplum has powerful analytical tools necessary to help you draw additional insights from your data. It is used across many applications, including finance, manufacturing, education, retail, and so on. Some of the well-known companies using Greenplum are Walmart, American Express, Asurian, Bank of America, etc. Besides them, it is even used in professional services, automotive, media, insurance, and retail markets.
It is specially designed to manage large-scale data warehouses and business intelligence workloads. It allows you to spread your data out across a multitude of servers.
The architecture is based on an MPP database. It means it uses several different processing units that work independently using their own resources and dedicated memory—this way, the workload is shared across multiple devices instead of just one. MPP databases scale horizontally by adding more compute resources (nodes).
( Image source: DZone)
Just like PostgreSQL, Greenplum leverages one master server, or host, which is the entry-point to the database, accepting connections, and SQL queries. Unlike PostgreSQL that uses standby nodes to geographically distribute their deployment, Greenplum uses segment hosts which store and process the data.
Advantages of the Greenplum Database
The Greenplum is undoubtedly a great database, but it is competing against some strong contenders like Amazon Redshift and Impala. The Greenplum usability and prominence would mostly rely on how quickly they introduce the latest technology in their model at lower rates or free.
Big data is a huge amount of data that has not been handled by the traditional data management systems.
Business Intelligence(BI) is a technique, tool required to collect, store, analyse data into valuable information and benefit from analysing and making efficient business decisions.
Offered by Microsoft, Power BI is a business analytics solution that enables business organizations visualize data and share key insights across the entire organization. Also, administrators can embed them in the application or website efficiently. Particularly, it connects to thousands of data sources and brings the data to activity with live and interactive dashboards and reports. It pulls data together and turns into intelligible insights using easy-to-process charts and graphs. Moreover, it connects to an array of data sources, right from basic Excel sheets to databases to cloud-based software solutions and on-premise applications.
Hence, calling it a data connection technology is justified with this service of Power BI. Here, connecting with leading Microsoft partners can help enterprises draw maximum benefits out of this extensive business intelligence capability.
Companies today, small, medium, and large are using business intelligence solutions in their businesses to make effective data-driven decisions. Large companies are bombarded with information overload, and through the tools provided by business intelligence solution company they can control, understand, analyze their data.
Business intelligence solutions are essential because they help managers and business owners make smart decisions and meet their sales and marketing targets. The tools provide a means of interacting with their customers giving them a deeper understanding of their client’s needs and fulfilling them effectively. Business intelligence solutions enhance data security. They also help eliminate the time-consuming task of compiling data manually, thus, saving time.
When choosing a company that provides you with business intelligence services in India, you have to pay attention to data modeling, data visualization, reporting, support, big data integration, deployment environment, and native security.
If you are looking to improve productivity in your company and save time when going through vast piles of data, adopts any of the top 10 business intelligence solutions discussed below.
Birst provides its users with a cloud-based analytics solution that helps them discover insights without the use of analytical input. It allows users to pinpoint patterns and understand their company’s key performance indicators.
The Birst tool features an automated data refinement that merges data from multiple sources into one user-ready data. It also allows real-time access to data. Birst also has an adaptive UX, interactive dashboards, multi-tenant cloud architecture, machine learning, and one-click data connectivity. It also has a mobile application for androids and iOS through which users can access their reports and dashboards.
Not suitable for complex analysis, does not support non-English languages, and lacks timely support.
Board is a data discovery platform that is used by small, medium, and large companies. It provides users with business intelligence, business analytics, and enterprise performance management under a single platform. Board has customizable and interactive dashboards that enable the users to see a complete overview of their business. They can also analyze their business's KPIs to asses their performance objectives.
Board features an in-memory technology known as Hybrid Bitwise Memory Pattern that offers the high-performance capability to read and write, quick data visualizations, simulations that and planning processes. It allows users to export data in several useful formats such as HTML and CSV. Board is multilingual such that it provides different languages.
Dunda BI is a product od Dunda Data Visualization. It allows the users to gather data from multiple sources and then generate interactive dashboards, customize their visualizations, and build reports in the form of charts and graphs. These charts and graphs enable them to identify and predict pattern trends in their organizations. This allows users to make informed decisions and implement best practices in their business.
With the use of the Dundas BI interface, users can easily create a wide range of reports such as automated or customized, and the recycle bin features make it easy to restore documents once deleted.
Dundas BI features API support and HTML 5 foundation.
Small, medium, and large companies can use Sisense. It focuses on data discovery, analysis, and intelligence, and every user can access the data through embeddable, scalable, accessible architecture. Sisense has a back end powered in chip technology that allows users to combine data from multiple sources into a single database. This allows front end users to create visuals, reports, and dashboards. It also enables the sharing of information between the users.
Sisense features In-chip analytics, Prism10X that allows users to analyze data 10 times faster than with any other in-memory solution. The single-stack system enables users to perform a range of tasks on the same platform. Machine learning and integration.
QlikView Platform focuses on data discovery and customer insight. The tool is from Qlik, which is a leader in the idea and intelligence space. It is easy to access and offers self-service data that help users make decisions and generate personalized reports and custom dashboards.
QlikView is affordable and can be used in any business. It designed in a way that can connect to any source of data such as big data streams, cloud data, or file-based data. Its patented in-memory data processing feature processes the data into as little as 10% of its initial size.
QlikView allows collaboration across a user’s interface such that two people can share the same dashboard. The patented in-memory application enables users to conduct quick searches and eliminates the issue of slow, on-disk applications.
It does not allow users to identify patterns within the data, and users can not predict project profitability.
It is a great collaboration tool as it allows users from different departments to pull together data from sales, support, logistics, and e-commerce. It easy to use and permits the sharing of information from one department to another. You do not need to be a tech guru to use Looker.
Looker provides users with self-service tools, such as filtering, pivoting, and visualizations. Looker’s dashboard gives the user a quick and clear view of insights.
Looker is not limited to any web browser, and it can also be installed in a mobile device, giving users mobility in collaboration.it also allows real-time updates.
Looker can be used in the healthcare, financial, technology industry.
Microstrategy is used by both small and medium-sized companies. It is a popular business intelligence solution because it is easy to use. Besides, it has scalable and sophisticated analytics functions.
Microstrategy has unique features that help it stand out, such as social intelligence, app integration, mobile productivity, data discovery, and real-time telemetry. It allows users to combine data from more than 200 sources and then visualize them.
Microstrategy can be downloaded on the phone, making it easy to read and share data on the go. It is also designed for use by any business size since it can be operated from a desktop, mobile. It offers users of all levels with business intelligence services. Major clients include Merck, AIG, Coca-Cola, and Vibes.
It does not support portal integration, and training modules are costly. It does not point out the issues in the analyzed data.
Zoho is a self-service BI and data analytics platform. Zoho allows the user to incorporate data from several sources and blend it, to generate reports and dashboards. The BI solution features a drag and drop, eliminating the need to download and upload data. It also has various visualization tools that allow the user to drill down to specifics. It provides machine learning natural language processing.
Before investing in any of the following business intelligence solutions also consider the following, subscription, maintenance, installation, customization, data migration, and renewal costs. You should also look at the features and choose according to the needs of your business.
If you look around you, a lot of things have been operating on wireless technology like mobile phones, TV, music players, flying drones, Alexa, and even autonomous cars. The successful implementation of wireless technology among these devices was possible because of high computation power, rapid data processing, and miro-sensors.
The invisible technology is notifying its prominent presence in almost all areas, and its approval has never had to wait for any testimonials. AI and cloud computing have refined these technologies further to perform better. Cloud computing is in complete transition form with edge computing.
What is edge computing?
The ‘Edge’ in over here is referred for computing infrastructure closer to the source of data. It means the data is stored in local computers and storage devices (IoT itself), rather than routing all the information through a centralized Data Center in the cloud. It includes storage, computes, and network connectivity.
( Image source: alibabacloud)
The edge computing spans an array of technologies like,
In the traditional model of IoT, all the devices are connected to a central server. The distant cloud environments are not ideal for latency-sensitive and bandwidth-hungry applications. Cloud computing bears few limitations due to central storage systems like data security threats, operational costs, and performance issues. In edge computing, the data is closer to the end-user, often on-premise or near a network access point.
Example of edge computing
The next-gen computing will be influenced by a lot by Edge Computing. Some of the best applications of edge computing will be in the health sector and autonomous vehicles. The sensor in an autonomous vehicle does not have to rely on a remote server to make a life-saving decision. The IoT device itself is empowered to provide data for making decisions.
Edge computing is also allowing drone management for unmanned maintenance and virtual fraud detection for banking, retail entertainment, and more. You call this a coincidental or perfect timing; the edge computing complements IoT devices for its maximum operability.
Impact of edge computing on IoT
Edge computing will specifically improve the IoT performance as it will help to address the pain-points of IoT.
(Image source: wildnettechnologies)
Some of the leading players in Edge computing product and services are:
Edge computing and IoT are quickly becoming the norm in the digital world. With improved internet speed (5G), lower prices, and better security, the IoT and edge computing are set to transform the current business processes.
Data science and Python are a perfect union of modern science. You may call it a coincidence or technology revolution phase, the fact is: they resonate with each other perfectly. Their camaraderie helped data-scientists to develop some best scientific applications that involved complex calculations. The object-oriented approach of Python language gels well with Data Science.
Data science spans three designations for the professionals interested in this field,
1) Data Analysts
2) Data Scientists
3) Data engineers
These professionals are highly talented and capable of building complex quantitative algorithms. They organize and synthesize large amounts of data used to answer questions and drive strategy in their organization.
Steps to learn data science with Python
Step 1) Introduction to data science
Get a general overview of Data Science. Then learn how Python is deployed for data science applications and various steps involved in the Data Science process like data wrangling, data exploration, and selecting the model.
Step 2) Having a good hold over Python language and their libraries
Complete knowledge of Python programming language is essential for data-science, particularly the scientific libraries.
Learn Scientific libraries in Python – SciPy, NumPy, Matplotlib and Pandas
Step 3) Practise Mini-Projects
The data-science enthusiasts on initial bases can improve their knowledge by working with Mini-Projects. While working with a mini-project, try to learn advanced data science techniques. You can try machine learning – bootstrapping models and creating neural networks using scikit-learn. .
There are many online sources free as well as paid that could assist you in learning data science with Python.
Here is the list of free courses to learn Data Science with Python
1) Computer Science & Programming Using Python
Offered by: MITx on edX
Duration: 9 weeks
Skill level: Introductory
Technology requirements: Basic algebra and some background knowledge of programming
2) Statistics With Python Specialization
Offered by: University of Michigan on Coursera
Duration: 8 weeks
Skill level: Introductory
Technology requirements: Basic linear algebra & calculus
3) Data Science: Machine Learning
Offered by: Harvard on edX
Duration: 8 weeks
Skill level: Introductory
Technology requirements: An up-to-date browser to enable programming directly in a browser-based interface.
4) Data Science Ethics
Offered by: University of Michigan on Coursera
Duration: 4 weeks
Skill level: Introductory
5) Introduction to Python and Data-science
Offered by: Analytics Vidhya
Duration: Depends on course
Skill level: Intermediate
6) Data Scientist in Python
Offered by: Dataquest
Duration: Depends on course
Skill level: Intermediate to high level
Paid courses to learn Data-Science
From an absolute beginner to a pro in the journey of learning data science, you might be using all sets of skills or technology mentioned below. So, it is preferable to tap on these technology stacks as well.
(Image source: datascience.berkeley. edu)
Advanced humanoid robots are capable of simulating humans in all respects. Like darwin’s theory of evolution, they achieved this milestone through technological evolution. This was possible because we are getting better in communicating with electronics through high-level programming languages like Python.
A humanoid robot is just one of the instances; the magic of programming Python spans even to Galaxy. NASA uses Python to program its space equipment.
Python is extremely easy to handle. It enables programmers to write fewer lines of code and make it more readable. Even non-programmers can learn the Python language with ease. But what it is there that makes Python the best programming language for Big Data.
( Image source: MVHS)
(Image Source: geekmusthave)
The general misconception of Big data is that it is about the volume/size of data. But Big data is more than the volume or size. It is referred to the large amounts of data which is pouring in from various data sources and has different formats.
Usually, you gather data in these formats.
Later, this data is made more meaningful with data cleansing technique and used for various purposes like business process enhancement, customer acquisition, improving user experience, etc. Take the example of Netflix, which uses Big Data analytics to make shows and movie recommendations to its users.
There are few other sectors that uses Big Data involves Banking, Transportation, Health care units, Government Organization, and so on.
Big data is also described with its 5V’s- Volume- huge amount of data, Variety- different formats of data, Value- extract useful information from data, Velocity- accumulating data with speed, and Veracity- analysing uncertainty and inconsistency in data.
( Image source: edureka)
Reasons why Python is best for Big Data
4. Hadoop is a popular open-source big data platform. Its inherent compatibility with Python makes it a preferred language for Big data
5. Scalable applications can be created with python programming. Python also has the ability to integrate itself with web applications very easily.
6. It is more preferable when data-analytics is required.
Data Visualization : Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
Before we discuss the two main BI tools below, it is important to take a moment to understand why these tools can help your organization.
Business Intelligence is part of data analytics. BI uses data to help organizations make smarter decisions based on past results. Because of this focus on the past, business intelligence is often called descriptive analytics since it describes what already happened in the organization.
The main benefit of BI tools like the ones below is they aggregate the data in a central visual dashboard. Businesses can share these dashboards with their management teams as reports.
Many BI tools today have expanded past the basic visual dashboards they were in the past to include predictive analytics features. Predictive analytics predicts enterprise’s future events based on past events and artificial intelligence. As organizations send more data to their business intelligence solution, its power of prediction increases.
By looking at the organizations’ story, executives can decide the best course of action. As BI tools improve, they learn how to help executives improve their decisions. This is called prescriptive analytics.
Prescriptive analytics examines the possible outcomes from each recommendation and then offers what the computer believes is the best outcome possible.
Tableau vs Microsoft Power BI
Description- Like the other BI tools we mentioned above, Tableau transforms data into actionable insights. They have a great tool for creating ad hoc analyses and visual dashboards.
Benefits– The Tableau Creator has great visualization features and is easy-to-use. They started offering free services for a year to teachers and students with the COVID-19 pandemic.
Other features and benefits include:
Challenges– Unlike the other BI tools, Tableau can only do reporting. They do not have any ETL features. Therefore, they are not as dynamic when it comes to data transformation.
2. Microsoft Power BI :
Description– Part of Microsoft’s Power Platform, Power BI gives everyone in an organization the ability to design applications and manage data without having a master’s degree in IT. Furthermore, Microsoft Power BI Services presents information in a specific format.
Benefit– Because Microsoft owns Power BI, it is a core part of the Microsoft product ecosystem.
For example, we helped an Australian family-focused NGO set up a Power App where remote team members could enter valuable data about program attendees. We connected the Power App to Power BI, so they could analyze each program’s success in one place.
Organizations value the powerful data visualizations that help them improve their decision-making. Other benefits and features include:
Challenges- While anyone can use Power BI, there is still a learning curve. Often, it helps to have a Power BI expert. Additionally, the basic standalone pricing starts at $9.99/mos. However, some of the advanced premium versions are too expensive for many SMBs.
Also, complex business use cases might not be able to use this program due to the table relationships, rigid formulas, and interrelated Microsoft 365 tools.
Conclusion: All the data visualization tools serves the same purpose but Microsoft Power BI comes with some additional features than tableau even if you are looking for more advanced predictive analytics than you should go for Microsoft Power BI.
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