Fast Data with Apache Ignite and Apache Spark with Christos Erotocritou
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The document discusses Apache Ignite, a distributed in-memory platform that can be used with Apache Spark. It provides powerful APIs and flexible processing capabilities. Ignite allows for memory-centric storage and processing of data across clusters. It also integrates with Spark by allowing RDDs and DataFrames to be created from Ignite caches. This enables capabilities like running SQL queries and sharing data globally across Spark jobs. GridGain is a commercial distribution of Apache Ignite that adds enterprise features.
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#EUstr10SPARK SUMMIT
• IgniteContext is the main entry point to Spark-Ignite integration:
val igniteContext = new IgniteContext[Integer, Integer]
(sparkContext, () => new IgniteConfiguration())
val cache = igniteContext.fromCache("myRdd")
val result = cache.filter(_._2.contains("Ignite")).collect()
val cacheRdd = igniteContext.fromCache("myRdd")
cacheRdd.savePairs(sparkContext.parallelize(1 to 10000, 10).map(i => (i, i)))
• Saving values to Ignite:
• Running SQL queries against Ignite Cache:
val cacheRdd = igniteContext.fromCache("myRdd")
val result = cacheRdd.sql
("select _val from Integer where val > ? and val < ?", 10, 100)
• Reading values from Ignite:
Working with IgniteRDD](https://image.slidesharecdn.com/la9christoserotocritou-171031210440/85/Fast-Data-with-Apache-Ignite-and-Apache-Spark-with-Christos-Erotocritou-11-320.jpg)
 =>
new IgniteConfiguration()).fromCache("CompanyCache")
val dfCompany = sqlContext.createDataFrame(companyCacheIgnite.map(p=>
Company(p._1, p._2)))
dfCompany.registerTempTable("company")
Working with DataFrame API
• Create an IgniteRDD
• Create a “Company” DataFrame
• Register DataFrame as a table](https://image.slidesharecdn.com/la9christoserotocritou-171031210440/85/Fast-Data-with-Apache-Ignite-and-Apache-Spark-with-Christos-Erotocritou-12-320.jpg)
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Boost Business Efficiency with Professional Data Entry Services
Boost Business Efficiency with Professional Data Entry Serviceseloiacs eloiacs Boost Business Efficiency with Professional Data Entry Services
In today’s digital-first world, businesses generate and handle massive amounts of data every day — customer records, sales data, inventory logs, survey results, and much more. But raw data has no value unless it is well-organized, accurate, and easily accessible. That’s where professional data entry services come in.
By outsourcing data entry tasks to experts, businesses can streamline operations, reduce manual errors, and improve overall efficiency — while focusing their internal resources on core activities like growth and customer engagement.
What Are Data Entry Services?
Data entry services refer to the process of converting information from various formats (handwritten, scanned, PDF, image, or audio) into structured, digital formats such as Excel sheets, CRM databases, or cloud storage systems. This work may be done online or offline, manually or using automation tools, depending on the client’s requirements.
Popular Data Entry Services Include:
Manual data entry from paper documents or scanned files
Online data entry directly into websites, forms, or portals
Offline data entry in formats like MS Word, Excel, or custom software
Product data entry for eCommerce platforms like Shopify, Amazon, and Flipkart
Document and image conversion into editable formats
Data cleansing and validation to remove duplicates and fix errors
Remote data entry support for real-time operations
CRM and ERP data management
These services are essential for organizing business data and making it usable for analysis, reporting, and decision-making.
Who Can Benefit from Data Entry Services?
Outsourcing data entry is not limited to any one industry — it's a universal need for businesses of all types and sizes. Here are some examples:
eCommerce Businesses – For managing product catalogs, inventory updates, pricing, and customer orders.
Healthcare Providers – For digitizing patient records, prescriptions, and insurance documents.
Education Institutes – To maintain student records, exam results, and staff data.
Financial Services – For processing invoices, bank statements, transaction records.
Real Estate Companies – To handle property listings, legal paperwork, client records.
Research & Marketing Firms – To compile survey data, leads, and analytics reports.
Even startups and freelancers often require virtual data entry services to stay organized and competitive.
Top Benefits of Outsourcing Data Entry Services
Outsourcing data entry work to a professional company or virtual assistant offers multiple benefits — whether you're running a small business or managing a large enterprise.
1. Reduced Costs
Maintaining an in-house data entry team means salaries, hardware, training, and software expenses. Outsourcing eliminates these costs and provides flexible, pay-as-you-go solutions.
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Starbucks in the Indian market through its joint venture.
Starbucks in the Indian market through its joint venture.sales480687 This topic focuses on the growth and challenges of Starbucks in the Indian market through its joint venture with Tata. It covers localization strategies, menu adaptations, expansion goals, and financial performance. The topic also examines consumer perceptions, market competition, and how Starbucks navigates economic and cultural factors in one of its most promising international markets.
Fast Data with Apache Ignite and Apache Spark with Christos Erotocritou
- 1. Christos Erotocritou, GridGain Systems Fast Data with Apache Ignite & Apache Spark #EUstr10
- 2. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT …is a distributed, memory-centric data platform with powerful & flexible processing APIs
- 3. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT Apache Ignite Memory-Centric Data Platform Ignite Memory-Centric Storage Ignite Native Persistence (Flash, SSD, Intel 3D XPoint) Third-Party Persistence (RDBMS, HDFS, NoSQL) SQL Transactions Compute IgniteRDD MLStreamingKey/Value IoTFinancial Services Pharma & Healthcare E-CommerceTravel & Logistics Telco Applications
- 4. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT Memory-Centric Storage
- 5. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT Pure Ignite Deployment Front-End APIs SQL TXCompute Ignite RDD Key / Value Payments SecuritiesRisk Trading Clients Ignite Cluster DURABLE MEMORY DURABLE MEMORY DURABLE MEMORY Data Caches / Tables Applications in Java, .NET & C++ Wide Range of Data Access and Processing APIs Shared Storage across Apps & Support for Multi- Tenancy Disk & Memory Data Storage
- 6. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT Durable Memory Ignite Server Cluster Off-heap Removes noticeable GC pauses Automatic Defragmentation Stores Superset of Data Predictable memory consumption Fully Transactional (Write-Ahead Log) DURABLE MEMORY DURABLE MEMORY DURABLE MEMORY Server Node Server Node Server Node Memory-Centric Storage Instantaneous Restarts
- 7. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT Apache Ignite Features JCache Compute Transactions Scan & Text QueriesSQL JDBC & ODBC StreamingServices Java .NET C++ PHP BI ToolsMemcached REST DURABLE MEMORY DURABLE MEMORY DURABLE MEMORY DURABLE MEMORY Distributed Memory-Centric Storage Dynamic Scaling Server Nodes
- 8. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT 1.Initial Request 2.Fetch data from remote nodes 3.Process the entire data-set 1.Initial request 2.Co-locate processing with data 3.Reduce multiple results into one Client-Server Processing Co-located Processing 2 1 Data & Processing Node Data & Processing Node Client Node 33 Data 1 Data NodeData 2 Data Node Processing Node 1 2
- 9. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT Hadoop, Spark & Ignite Deployment SQL & Compute APIDB File Exports Ignite Clients Kafka Data Streamer Ignite Data Streamer Spark App Hadoop Data Node Spark App Hadoop Data Node Spark App Hadoop Data Node Spark Clients Server Nodes IgniteRDD IgniteRDD IgniteRDD
- 10. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT Apache Ignite Spark Integration Spark Application Spark Worker Spark Job Spark Job Yarn Mesos Docker HDFS Spark Worker Spark Job Spark Job Spark Worker Spark Job Spark Job In-Memory Shared RDD or DataFrame Share RDD across jobs on the host In-Memory Indexes SQL on top of RDDs Share RDD Globally Ignite Node Ignite Node Ignite Node
- 11. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT • IgniteContext is the main entry point to Spark-Ignite integration: val igniteContext = new IgniteContext[Integer, Integer] (sparkContext, () => new IgniteConfiguration()) val cache = igniteContext.fromCache("myRdd") val result = cache.filter(_._2.contains("Ignite")).collect() val cacheRdd = igniteContext.fromCache("myRdd") cacheRdd.savePairs(sparkContext.parallelize(1 to 10000, 10).map(i => (i, i))) • Saving values to Ignite: • Running SQL queries against Ignite Cache: val cacheRdd = igniteContext.fromCache("myRdd") val result = cacheRdd.sql ("select _val from Integer where val > ? and val < ?", 10, 100) • Reading values from Ignite: Working with IgniteRDD
- 12. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT val companyCacheIgnite = new IgniteContext[Int, String](sc, () => new IgniteConfiguration()).fromCache("CompanyCache") val dfCompany = sqlContext.createDataFrame(companyCacheIgnite.map(p=> Company(p._1, p._2))) dfCompany.registerTempTable("company") Working with DataFrame API • Create an IgniteRDD • Create a “Company” DataFrame • Register DataFrame as a table
- 13. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT – Ingests data from HDFS or another distributed file system – Inclined towards analytics (OLAP) and focused on MR-specific payloads – Requires the creation of RDD and data and processing operations are governed by it – Basic disk-based SQL support – Strong ML libraries – Big community – Data source agnostic – Fully fledged compute engine and durable storage – OLAP & OLTP – Zero-deployment – In-Memory SQL support – Fully ACID transactions across memory and disk – Less focused on Hadoop – Early ML Support – Growing Community
- 14. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT • What is GridGain? • Binary build of Apache Ignite™ • Added enterprise features for enterprise deployments • Earlier features and bug fixes by a few weeks • Fully certified & tested releases “We develop and support the worlds leading In-Memory Computing Platform”
- 15. © 2017 GridGain Systems, Inc. #EUstr10SPARK SUMMIT Thank you for joining us. Follow the conversation. http://ignite.apache.org Any Questions?