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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,
- Thick Client (a.k.a. Client Node)
- Thin Client
- JDBC and ODBC Drivers
- REST API
You can pick client connectors based on the following criteria.
- Thick Client: You can choose a thick client when the application resides in the same environment where server nodes run. It is advisable to use only when there is full network connectivity between the application and every server node (i.e., no firewall or NAT). Thick clients are usually implemented in computing environments when the primary server has low network speeds, limited computing, and storage capacity to facilitate client machines, and need to work offline. It should be your first choice as they provide the most functionality. The thick clients are available only for JVM languages, .NET languages, and C++.
- Thin Client: If your application runs on a device with limited resources, and operated remotely, use the thin client. Ignite provides implementations written in Java, .NET, C++, Python, NodeJS, and PHP.
- JDBC or ODBC drivers: if the application has to use standard JDBC or ODBC API, then use the corresponding SQL driver.
- Rest API: Apache ignite also comes with the REST API. It helps in performing basic operations like executing SQL queries, reading or updating cache entries, etc. However, this API is not suitable for any performance-sensitive purposes.
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
- High Performance: Greenplum has a uniquely designed data pipeline that can efficiently stream data from the disk to the CPU, without relying on the data fitting into RAM. Greenplum’s high performance overcomes the challenge most RDBMS have scaling to petabyte levels of data. It enables you to run analytics directly in the database rather than exporting and running the data in an external analytics engine; this further enhances the performance of the data analysis.
- Query Optimization: The Greenplum system ensures the fastest responses to all the queries. The Greenplum distributes the load between their different segments and uses all of the system’s resources parallel to process a query. The single query performance has been optimized in Greenplum 6 with the improved OLTP workload capacity. It can query external data sources like Hadoop, ORC, Cloud Storage, Parquet, AVRO, and other Polyglot data stores.
- Open source: The big advantage of the Greenplum database is that it is an open-source data warehouse project based on PostgreSQL. Since it is open-source, it allows users to get all the advantages that PostgreSQL provides. Greenplum can run on any Linux server, whether it is hosted in the cloud or on-premise, and can run in any environment. Unlike the Oracle database that runs on almost all servers, the Plumb database is limited to Linux servers only. This could be one of the areas where Greenplum has to work in the future.
- Support for containerization: Greenplum exhibits excellent support for the container model. It can containerize “segments” that are logically isolated workloads and groups of resources. Its support for containerization further facilitates deployment techniques such as champion/challenger or canaries.
- AI and Machine Learning: The Greenplum v6 adds more machine learning support and clears the way for deep learning. Greenplum's ability to process large volumes of data at high speeds makes it a powerful tool for smart applications that need to interact intelligently based on an unlimited number of unique scenarios.
- Polymorphic Data Storage: The polymorphic data storage enables you to control the configuration for your table. It also gives the liberty to partition storage and compress files within it at any time.
- Integrated in-database analytics: Apache MADlib is an open-source, SQL-based machine learning library that runs in-database on Greenplum. The library extends the SQL capabilities of the Greenplum Database through user-defined functions. Besides that, users can use a range of power analytics tools with Greenplum like R statistical language, SAS, and Predictive Modeling Markup Language (PMML).
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 field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
Business Intelligence is a set of strategies and tools that companies can employ to handle business data analysis.
In other words, Business Intelligence is the process of comprising technologies and strategies incorporated by the enterprise industries to analyze the existing business data, which provides past (historical), current and predictive events of the business operations.
The main purpose of Big Data is to capture processes and analyze the data to improve outcomes.
The main purpose of Business Intelligence is to help businesses to make better 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.
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,
- Wireless sensor networks
- Cloud/fog computing
- Distributed data storage and retrieval
- Autonomic self-healing networks
- Remote cloud services
- Augmented reality
- Cooperative distributed peer-to-peer ad-hoc networking and processing classifiable as local
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)
- Low latency services: In a data-driven world, robust network connectivity is essential for rendering low-latency services. Edge infrastructure technology collects, processes, and reduces the enormous quantities of data. The edge computing allows IoT devices to function, even when there is limited or intermittent connectivity to the wider cloud computing network.
- Security: Security vulnerabilities in IoT is a chief barrier for its implementation, especially in smart homes. Since the data is stored locally to a device, it provides designers with more opportunities to protect the data as soon as it is gathered with the use of memory encryption as well as dedicated security hardware. Also, the sensitive data is less exposed to potential sources of attackers. With edge computing, various security parameters can be implemented like intrusion detection systems, distributed firewalls, authentication and authorization algorithms, and privacy-preserving mechanisms.
- Low operational costs: Edge computing makes your data more relevant, useful, and actionable. It reduces the overall traffic loads of your enterprise, and it improves performance for all of your enterprise applications and services.
- Speed: Edge computing makes your data more relevant, useful, and actionable. It reduces the overall traffic loads of your enterprise and it improves performance for all of your enterprise applications and services.
- Scalability: Scalability is an absolute necessity for the success of the IoT. It means the IoT networks connected with edge computing should be able to add more devices and handle traffic in real-time. Edge data centers allow enterprises to efficiently support their end-users with little physical distance or latency. The edge computing’s scalability is an attractive proposition for companies implementing digital transformation.
- Reliability: With edge computing, IoT users can rely more on their usability. The IoT edge computing devices and with edge data centers positioned closer to end-users, there is less chance of a network problem.
- Support for 5G network: 5G will mark the rise of Edge Computing, and it will help the telecommunication and wireless communication models to a great extent as such it forms a major part of IoT infrastructure. It will reduce the latency in the 5G network.
Some of the leading players in Edge computing product and services are:
- ADLINK Technology
- Dell EMC
- Hitachi Vantara
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.
Big data analytics is a form of advanced analytics that encompasses complex applications with predictive models, statistical algorithms, and what-if analysis powered by high-performance analytics systems.
Implementing Big data analytics to your business can help your business progress with:
- Fresh revenue opportunities
- More efficient marketing
- Superior customer service
- Improvised operational efficiency
- Competitive benefits over rivals
1. Sigma Data Systems
Sigma Data Systems is one of the leading big data analytics companies in Bangalore that understands the cruciality of each piece of data in today’s world. The company also conducts pre-defined workshop patterns to understand the problems faced by the clients and provides out of the box solutions to each of their clients by utilizing various tools and techniques.
2. COLTFOX PRIVATE LIMITED
Coltfox is one of the most popular companies in Bangalore that provides big data solutions to organizations. These services help the organizations in making their products, services, and marketing communication more accessible, useful, and reliable for everyone. Coltfox offers creative insight and commercial awareness that its client requires to transform their business. The company provides imaginative design and smart branding services to its clients.
3. Focaloid Technologies
Based in Bangalore, Focaloid is a big data analytics company that focuses on developing value-adding technology solutions with user-engaging designs to its clients. Big data solutions provided by the company solve numerous problems faced by businesses. It helps in cost reductions, improvising operational efficiency, smart decision making, and new product development. Focaloid Technologies works on the approach of combining Big data with high-powered analytics, which proves to be useful for the growth of their client’s business.
4. FoOfys Solutions
Headquartered in Bangalore, Foofys Solution is an excellent big data company that provides its clients with a vision of sustainable business solutions for the progress of their company. The tech-savvy team of designers, developers, innovators, and hackers at the company helps organizations with advanced big data analytics solutions.
Sourcebits is a well established Big data analytics company in Bangalore which refines ideas, solve business problems, and align teams to provide the best solutions to its clients. The team of developers in the company has mastered the ability to process massive amounts of data and generate KPIs that help in delivering the best business outcomes to its clients. The company also provides:
- Enhanced operational efficiencies
- Increased customer segmentation that enables personalized and conversational marketing
- Prime focus to cyber-security
- Real-time data to customers and internal teams
Sourcebits offers accessible, real-time, ingestible, and retrievable data-driven solutions & decisions.
6. BrandstoryBrandStory is a Big data analytics company that is reputed in the industry for creating a unique brand identity for each of its clients. The company makes the brand identity possible by digitally defining the client’s ideas. Brandstory also focuses on getting its client’s products & services to the ever-expanding digital market by increasing brand awareness and sales.
Informatica is a big data analytics company in Bangalore that delivers trusted information for analytics of its client’s business. The company majorly focuses on delivering transformative innovation for the future of all things data. Informatica provides information potential and drives top business imperatives to various organizations across the globe.
Numerify is an excellent big data analytics company that grasps the fastest route to authorize business users to get analytics by utilizing packaged applications. The Numerify AI-powered analytics solutions deliver the augmented intelligence that provides its client’s business with accelerating delivery, operational automation, and higher reliability. The big data IT solutions provided by the company are platform-driven, focused on customer success, and capable of running for weeks.
Manthan is an AI-equipped big data management & analytics company that provides large scale, performance-driven, reliable, and secure services on the cloud. The company offers the fastest ROI with extensive infrastructure provisioning capability.
Quantzig is an analytics and advisory firm that operates from offices in the US, UK, Canada, China, and India. The company provides end-to-end data modeling capabilities to its clients worldwide, which helps them for prudent decision making. Quantzig focuses on gaining maximum insights from the influx of continuous information. This valuable data in turn help organizations to achieve success.
I have classified a few of the companies based on their hourly rate, number of employees, year of establishment, and the countries they have offices in:
You can opt for the company which best fits your requirements from the list of all the companies mentioned here.
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
- Practice the NumPy thoroughly, especially NumPy arrays.
- Go through the basics and practice SciPy
- The next stage is to get hands-on Matplotlib. It is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib can be used in Python, Python scripts, and IPython shell, web application servers, and various graphical user interface toolkits.
- Finally, brush your knowledge on Pandas. It provides DataFrame functionality (like R) for Python. It is recommended that you spend a good time practicing Pandas. It would become the most effective tool for all mid-size data analysis.
- Also, learn machine learning and natural language processing with Sci-kit.
- It is an advantage if you have a clear concept of K-Means Clustering, Logistic Regression, and Linear Regression. It is very valuable with respect to preparing a machine learning algorithm
- The individual should also hone their skills in web scraping with BeautifulSoup. Python integration with Hadoop MapReduce and Spark.
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
- Udemy- Python for Data Science and Machine Learning Bootcamp
- Intellipaat- Python for Data Science
- Udacity- Programming for Data Science with Python
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.
- Unstructured data: Audio, video files etc.
- Semi-Structured data: XML, JSON
- Structured data: RDBMS
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
- Python does not need to be compiled as it is an interpreted language . Interpreter actually parses the program code for generating the output.
- In Python, variable types are defined automatically.
- It supports an advanced library to implement machine learning algorithms. This is an advantage for the science community that deals with the scientific data.
- : You can call this a science-geek library. It supplies an extensive library of high-level mathematical and numerical functions
- : It is a multi-platform data visualization library and feeds huge amounts of data in an easily digestible visuals
- : Scikit-learn provides a range of supervised and unsupervised learning algorithms.
- : It allows various data manipulation operations such as groupby, join, merge, melt, concatenation as well as data cleaning
- : Developed by Google’s team this Machine Learning library is used for research in deep neural networks
- : It contains algorithms for neural networks.
- :It supports linear algebra, interpolation, FFT, ODE solvers, signal & image processing that is essential for scientific and technical computing
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:
- Easy-to-use drag & drop products
- Integrations with spreadsheets, databases, Hadoop, and cloud services
- Web and mobile dashboard share features
- Data preparation and governance add-on
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:
- Better, flexible insights
- Reduced Cost (Available in E5 plan or as a standalone tool)
- Built-in AI capabilities
- Excel, Teams, SharePoint, and other SaaS integrations
- Prebuilt and custom data connectors
- Enterprise level security and data loss prevention capabilities
- No or little technical experience needed
- Automate data prep and reporting processes
- iOS, Android, and Windows mobile apps
- Certifications (new feature)
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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