Designing a Real Time Data Ingestion Pipeline
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In this presentation, Badar discusses DataScience Engineering’s process for designing a real time data ingestion pipeline, facing different technical challenges and changing business needs along the way. Badar will also discuss how big data technologies like Kafka/Kinesis, Spark/Hadoop, and Cassandra/DynamoDB help solve problems for high throughput data ingestion & processing.
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Maybe this is a wild assumption, but my betting is you’d love your business to grow. The problem is, of course, that growth is never easy and takes tenacity, a bit of luck and a ton of hard work. At Audacious Creative, we’ve been wrestling with this question and, after weeks of feeling like we were getting nowhere and growth was never going to happen for us, we hit upon something so simple and powerful that it’s transforming everything we think, feel and do. In this slideshare/ webinar I want to take you through a really honest journey of how we’ve wrestled with this issue, what we’ve (re)learned and are now on a very clear trajectory to real growth. I look forward to going on this journey with you.
Cuestiones para elegir una pc
Presentacion de Prezi convertida a .PDF
Cuestiones para elegir una pc.
Sistemas de Informacion.
Instituto Dr. Ramon Carrillo
2014
El juego de las perlas y los delfines
Este documento discute cómo la sociedad occidental y los medios de comunicación han contribuido al aumento de problemas relacionados con el peso. Señala que los mensajes publicitarios a menudo son contradictorios, instando a las personas a estar delgadas pero también a comer en abundancia. Esto puede confundir a los niños y empujarlos a tomar decisiones poco saludables sobre la alimentación. El documento luego propone tres pasos para abordar esto: 1) limitar el tiempo que los niños pasan viendo televisión y usando Internet, 2) evitar imponer
Planning and Optimizing Data Lake Architecture - Milos Milovanovic
Planning and Optimizing Data Lake Architecture - Milos MilovanovicInstitute of Contemporary Sciences
The main idea of a Data Lake is to expose the company data in an agile and flexible way to the people within the company, but preserve safeguard and auditing features that are required for the company’s critical data. The way that most projects in this direction start out is by depositing all of the data in Hadoop, trying to infer the schema on top of the data and then use the data for analytics purposes via Hive or Spark. Described stack is a really good approach for many use cases, as it provides cheaply storing data in files and rich analytics on top. But many pitfalls and problems might show up on this road, which can be easily met by extending the toolset. The potential bottlenecks will be displayed as soon as the users arrive and start exploiting the Lake. From all of these reasons, planning and building a Data Lake within an organization requires strategic approach, in order to build an architecture that can support it.Creating a Modern Data Architecture
The document discusses creating a modern data architecture using a data lake approach. It describes the key components of a data lake including different zones for landing raw data, refining it into trusted datasets, and using sandboxes. It also summarizes challenges of data lakes and how an integrated data lake management platform can help with ingestion, governance, security, and enabling self-service analytics. Finally, it briefly discusses considerations for implementing cloud-based and hybrid data lakes.
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Basic Computer Training in Ambala ! Batra Computer Centre
Batra Computer Centre is An ISO certified 9001:2008 training Centre in Ambala.
We Provide Basic Computer Training in Ambala. BATRA COMPUTER CENTRE provides best training in C, C++, S.E.O, Web Designing, Web Development and So many other courses are available.
10 Amazing Things To Do With a Hadoop-Based Data Lake
Greg Chase, Director, Product Marketing presents Big Data 10 A
mazing Things to do With A Hadoop-based Data Lake at the Strata Conference + Hadoop World 2014 in NYC.
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Building Continuously Curated Ingestion Pipelines
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StashFin provides personal loans to individuals in India through a web and mobile platform. They have originated over 620,000 loans since being founded in 2016. To scale their platform, StashFin moved from a monolithic architecture to a microservices architecture using AWS services. This included using S3 for storage, EKS for Kubernetes, and AWS Glue and Athena for analytics. They also designed a data pipeline on AWS to handle a large increase in loan applications. The pipeline uses Redis for caching, S3 as the data lake, and Athena for querying large amounts of data stored in S3. This has allowed for faster decisioning, higher reliability, and cost and performance benefits compared to managing their own infrastructure.
Building FoundationDB
FoundationDB is a next-generation database that aims to provide high performance transactions at massive scale through a distributed design. It addresses limitations of NoSQL databases by providing a transactional, fault-tolerant foundation using tools like the Flow programming language. FoundationDB has demonstrated high performance that exceeds other NoSQL databases, and provides ease of scaling, building abstractions, and operation through its transactional design and automated partitioning. The goal is to solve challenges of state management so developers can focus on building applications.
An Enterprise Architect's View of MongoDB
This document discusses how MongoDB can help enterprises meet modern data and application requirements. It outlines the many new technologies and demands placing pressure on enterprises, including big data, mobile, cloud computing, and more. Traditional databases struggle to meet these new demands due to limitations like rigid schemas and difficulty scaling. MongoDB provides capabilities like dynamic schemas, high performance at scale through horizontal scaling, and low total cost of ownership. The document examines how MongoDB has been successfully used by enterprises for use cases like operational data stores and as an enterprise data service to break down silos.
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The document discusses moving away from monolithic architecture as a system grows larger. It identifies that while monolithic architecture is simple initially, it becomes difficult to work with as the codebase and system grow in size. This leads to issues like slow delivery, inability to scale the system or team, and difficulty adopting new technologies.
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This document provides an overview of Big Data concepts including architecture, use cases, and examples. It discusses how Big Data can help tell what will happen, find unexpected relationships, monitor situations, and fix problems. Real examples mentioned include Google leaving China, input methods, Google ads, Netflix recommendations, and Walmart personalized offers. The Big Data architecture described includes collecting data, using queues to decouple systems, processing data in Hadoop clusters, storing data in data stores like HDFS, HBase and Elasticsearch, and visualizing results. Cloud services can also be used to build projects quickly.
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MongoDB is well-suited for applications that require:
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Data Warehouse or Data Lake, Which Do I Choose?
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
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In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
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Designing a Real Time Data Ingestion Pipeline
- 1. Designing Real-Time Data Ingestion Pipeline Badar Ahmed
- 2. About Us DataScience Inc. ▪ Data Science as a service ▪ Customers from Sonos to Belkin ▪ Ranked #1 among "Best Places to Work in Los Angeles for 2015" ▪ Visit datascience.com! 2 Badar Ahmed ▪ Software Engineer ▪ Background in high performance computing & cloud computing ▪ Work across the stack on Big Data problems
- 3. Importance of Data Ingestion ▪ Data ingestion is precursor to any analysis ▪ Characteristics: ▪ Reliable ▪ Correctness ▪ Speed ▪ Scalable 3
- 4. Types of Data Ingestion ▪ Broad topic with many different architectural patterns ▪ RealTime ▪ Batch ▪ Structured Data ▪ Unstructured Data 4
- 5. Ingestion Evolution @ DataScience 5 ▪ Legacy API existed ▪ But .. ✦ Expensive ✦ Ops Heavy ✦ Hard to scale ✦ No batch interface
- 6. 6
- 7. What was needed ▪ Scaleable ingestion system ▪ Batch Ingest ▪ Lower Ops and $$$ Cost 7
- 8. Idea #1 ▪ Asynchronous API ▪ Queue requests and process them later Pros: ▪ Fast ▪ Scaleable 8
- 9. 9
- 10. Issues with Idea #1 ▪ Failure introduces complexity ▪ Decoupled systems can be more difficult to debug ▪ User UX poorer if they need to keep track of async requests ▪ Lot of deviation from the simpler API model of ConnectHQ 10
- 11. Idea #2 ▪ Synchronous Batch so .. ✦ UX remains the same ▪ Use Concurrency to do parallel writes to datastore ✦ Caveat: Concurrent code is difficult to write & debug 11
- 14. 14
- 15. 15
- 18. Unit Testing with Mocks 18
- 19. More Testing 19
- 20. More Testing 20
- 21. Test & Refactor Cycle 21
- 22. Test & Refactor Cycle 22
- 23. 23
- 24. Questions? 24
- 25. Thank you.
- 26. Development 26