About

Now, available on AWS Marketplace, Streamsets is a software offers continuous Ingest technology for upcoming generations of big data applications. Streamsets’ enterprise-grade infrastructure accelerates the time-to-analysis by bringing unparalleled transparency as well as event processing to data in motion. In simple words, Streamsets is most commonly used for creating and visualizing data pipelines

StreamSets offers two products, the Data Collector and the Dataflow Performance Manager. DC allows users to build platform agnostic data pipelines while DPM controls multiple data flows within the visual user interface.

Streamsets big data integration basically delivers performance management solutions for data flows which feed the next generation of big data applications. With this data operations platform, users can competently develop batch as well as streaming data flows for operating them with full visibility and control.

Miri Infotech, one of the leading IT solutions provider is configuring StreamSets, a modern data ingestion solution which is embedded with Ubuntu 16.04 along with ready-to-launch AMI on AWS Cloud Network containing Data Collector, Hadoop, HBase, NoSQL, Messaging system and Search System.

One of the main features of StreamSets is that the developers can effortlessly build batch with a minimum of code while operators use a cloud-native product to aggregate dozens of data flows into topologies to manage them centrally.

StreamSets software is aimed to address the rising challenge of managing data in motion in the world marked by constant transformation, from data sources to data processing infrastructure and the data itself. The mission of Streamsets is to bring operational excellence to the management of data in motion to make quality data arrives on-time subsequently, accelerating analysis and decision making.

Streamsets have below mentioned connectors:

Hadoop systems
NoSQL databases
Search systems
Messaging systems

Usage/Deployment Instruction

Step 1: Open Putty for SSH

Step 2: Open Putty and Type <instance public IP> at “Host Name” and Type "ubuntu" as user name Password auto taken from PPK file

Step 3: Use following Linux command to start Streamset

Step 3.1: $ sudo vi /etc/hosts

Take the Private Ip address from your machine as per the below screenshot and then replace the second line of your command screen with that Private ip address

Step 3.2: Change ssh ‘ubuntu’ user to ‘root’

>> sudo su

>> cd /home/Ubuntu

Step 3.3: Change open file limit.

>> ulimit -n 40000

Step 3.4: Start StreamSets datacollector server.

>> streamsets-datacollector-3.0.0.0/bin/streamsets dc

Step 3.5: Now start Streamsets in the Browser.

Open the URL: http://<instance ip address>:18630/

IP address of the running EC2 instance.

Username: admin

Password: admin

Step 3.6: After login you will see the Streamsets Dashboard.

All your queries are important to us. Please feel free to connect.

24X7 support provided for all the customers.

We are happy to help you.

Submit your Query: http://www.miritech.com/ContactUs.aspx

Contact Numbers:

(510) 298-5936
(510) 870-8668

Contact E-mail:

support@miritech.com

Other Useful Links:

Blog: http://support.miritech.com/blog/streamsets-data-collector-miri-infotech/

Product Demo: https://youtu.be/K1AIQx6u8ZQ

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You can subscribe to an AWS Marketplace product and launch an instance from the product's AMI using the Amazon EC2 launch wizard.

To launch an instance from the AWS Marketplace using the launch wizard

Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.
From the Amazon EC2 dashboard, choose Launch Instance.
On the Choose an Amazon Machine Image (AMI) page, choose the AWS Marketplace category on the left. Find a suitable AMI by browsing the categories, or using the search functionality. Choose Select to choose your product.
A dialog displays an overview of the product you've selected. You can view the pricing information, as well as any other information that the vendor has provided. When you're ready, choose Continue.
On the Choose an Instance Type page, select the hardware configuration and size of the instance to launch. When you're done, choose Next: Configure Instance Details.
On the next pages of the wizard, you can configure your instance, add storage, and add tags. For more information about the different options you can configure, see Launching an Instance. Choose Next until you reach the Configure Security Group page.
The wizard creates a new security group according to the vendor's specifications for the product. The security group may include rules that allow all IP addresses (0.0.0.0/0) access on SSH (port 22) on Linux or RDP (port 3389) on Windows. We recommend that you adjust these rules to allow only a specific address or range of addresses to access your instance over those ports.
When you are ready, choose Review and Launch.
On the Review Instance Launch page, check the details of the AMI from which you're about to launch the instance, as well as the other configuration details you set up in the wizard. When you're ready, choose Launch to select or create a key pair, and launch your instance.
Depending on the product you've subscribed to, the instance may take a few minutes or more to launch. You are first subscribed to the product before your instance can launch. If there are any problems with your credit card details, you will be asked to update your account details. When the launch confirmation page displays

1What is Hadoop?

The Apache Hadoop software library allows for the distributed processing of large data sets across clusters of computers using a simple programming model. The software library is designed to scale from single servers to thousands of machines; each server using local computation and storage. Instead of relying on hardware to deliver high-availability, the library itself handles failures at the application layer. As a result, the impact of failures is minimized by delivering a highly-available service on top of a cluster of computers.
2Where does Hadoop find applicability in business?

Hadoop, as a scalable system for parallel data processing, is useful for analyzing large data sets. Examples are search algorithms, market risk analysis, data mining on online retail data, and analytics on user behavior data.
3What is big data security analytics?

Add the words “information security” (or “cybersecurity” if you like) before the term “data sets” in the definition above. Security and IT operations tools spit out an avalanche of data like logs, events, packets, flow data, asset data, configuration data, and assortment of other things on a daily basis. Security professionals need to be able to access and analyze this data in real-time in order to mitigate risk, detect incidents, and respond to breaches. These tasks have come to the point where they are “difficult to process using on-hand data management tools or traditional (security) data processing applications.”
4Is there an easy way to migrate data from Hadoop into a relational database?

The Hadoop JDBC driver can be used to pull data out of Hadoop and then use the DataDirect JDBC Driver to bulk load the data into Oracle, DB2, SQL Server, Sybase, and other relational databases.
5Are Intelligent Assistants the only application of AI for customer care, or are there other ways that AI technologies can impact the contact center?

Front-end use of AI technologies to enable Intelligent Assistants for customer care is certainly key, but there are many other applications. One that I think is particularly interesting is the application of AI to directly support — rather than replace — contact center agents. Technologies such as natural language understanding and speech recognition can be used live during a customer service interaction with a human agent to look up relevant information and make suggestions about how to respond. AI technologies also have an important role in analytics. They can be used to provide an overview of activities within a call center, in addition to providing valuable business insights from customer activity.
6What are the popular machine learning algorithms in use today?
There are many machine learning algorithms in use today, but the most popular ones are:
- Decision Trees
- Naive Bayes Classification
- Ordinary Least Squares Regression
- Logistic Regression
- Support vector machines
- Ensemble Methods
- Clustering Algorithms
- Principal Component Analysis
- Singular Value Decomposition
- Independent Component Analysis

Pipeline Data Collector	php	apache	mysql
linux	hadoop

Pipeline Data Collector

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Submit Your Request

1What is Hadoop?

2Where does Hadoop find applicability in business?

3What is big data security analytics?

4Is there an easy way to migrate data from Hadoop into a relational database?

5Are Intelligent Assistants the only application of AI for customer care, or are there other ways that AI technologies can impact the contact center?

6What are the popular machine learning algorithms in use today?

Highlights

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