Data Infrastructure, Data Pipeline and Analytics – Reading List – Sep 27, 2016

Splunk vs ELK: The Log Management Tools Decision Making Guide
Much like promises made by politicians during an election campaign, production environments produce massive files filled with endless lines of text in the form of log files. Unlike election periods, they’re doing it all year around, with multiple GBs of unstructured plain text data generated each day.
http://blog.takipi.com/splunk-vs-elk-the-log-management-tools-decision-making-guide/

Building a Modern Bank Backend
https://monzo.com/blog/2016/09/19/building-a-modern-bank-backend/

An awesome list of Micro Services Architecture related principles and technologies.
https://github.com/mfornos/awesome-microservices#api-gateways–edge-services

Stream-based Architecture
Part of the Stream Architecture Book. An excellent overview on the topic.
https://www.mapr.com/ebooks/streaming-architecture/chapter-02-stream-based-architecture.html

The Hardest Part About Micro services: Your Data
Of the reasons we attempt a micro services architecture, chief among them is allowing your teams to be able to work on different parts of the system at different speeds with minimal impact across teams. So we want teams to be autonomous, capable of making decisions about how to best implement and operate their services, and free to make changes as quickly as the business may desire. If we have our teams organized to do this, then the reflection in our systems architecture will begin to evolve into something that looks like micro services.
http://blog.christianposta.com/microservices/the-hardest-part-about-microservices-data/

New Ways to Discover and Use Alexa Skills
Alexa, Amazon’s cloud-based voice service, powers voice experiences on millions of devices, including Amazon Echo and Echo Dot, Amazon Tap, Amazon Fire TV devices, and devices like Triby that use the Alexa Voice Service. One year ago, Amazon opened up Alexa to developers, enabling you to build Alexa skills with the Alexa Skills Kit and integrate Alexa into your own products with the Alexa Voice Service.
http://www.allthingsdistributed.com/2016/06/new-ways-to-discover-and-use-alexa-skills.html

Happy Learning!

Data Infrastructure, Data Pipeline and Analytics – Reading List – Sep 20, 2016

Hadoop architectural overview
An Excellent series of posts – talking about Hadoop and Related components, Key metrics to monitor in Production
https://www.datadoghq.com/blog/hadoop-architecture-overview/
Surviving and Thriving in a Hybrid Data Management World
The vast majority of our customers who are moving to cloud applications also have a significant current investment in on premise operational applications and on premise capabilities around data warehousing, business intelligence and analytics. That means that most of them will be working with a hybrid cloud/on premise data management environment for the foreseeable future.
http://blogs.informatica.com/2016/08/19/surviving-thriving-hybrid-data-management-world/#fbid=dlbfZB7A1Sd
Data Compression in Hadoop
File compression brings two major benefits: it reduces the space needed to store files, and it speeds up data transfer across the network or to or from disk. When dealing with large volumes of data, both of these savings can be significant, so it pays to carefully consider how to use compression in Hadoop.
http://comphadoop.weebly.com/
 What is “Just-Enough” Governance for the Data Lake?
Just-enough governance is similar to the Lean Startup methodology concept of building of a Minimum Viable Product (MVP). From an enterprise perspective, just-enough governance means building only the process and control necessary to solve a particular business problem.
https://infocus.emc.com/rachel_haines/just-enough-governance-data-lake/
Mind map on SAP HANA
https://www.mindmeister.com/353051849/sap-hana-platform
Should I use SQL or NoSQL?
Every application needs persistent storage — data that persists across program restarts. This includes usernames, passwords, account balances, and high scores. Deciding how to store your application’s important data is one of first and most important architectural decisions to be made.
https://www.databaselabs.io/blog/Should-I-use-SQL-or-NoSQL
Happy Learning!

Data Infrastructure, Data Pipeline and Analytics – Reading List – Sep 12, 2016

Three incremental, manageable steps to building a “data first” data lake
Applications have always dictated the data. That has made sense historically, and to some extent, continues to be the case. But an “applications first” approach creates data silos that are causing operational problems and preventing organizations from getting the full value from their business intelligence initiatives.
http://www.networkworld.com/article/3098937/analytics/three-incremental-manageable-steps-to-building-a-data-first-data-lake.html

Azure SQL Data Warehouse: Introduction
Azure SQL Data Warehouse is a fully-managed and scalable cloud service.
https://www.simple-talk.com/cloud/azure-sql-data-warehouse/

The Informed Data Lake: Beyond Metadata
Historically, the volume and extent of data that an enterprise could store, assemble, analyze and act upon exceeded the capacity of their computing resources and was too expensive. The solution was to model some extract of a portion of the available data into a data model or schema, presupposing what was “important,” and then fit the incoming data into that structure.
https://hiredbrains.wordpress.com/2016/05/13/the-informed-data-lake-beyond-metadata/

Real Time Streaming with Spring xd, Apache Geode (Gemfire), and Greenplum
Spring xd is a unified, distributed, and extensible service for data ingestion, real time analytics, batch processing, and data export.
http://zdatainc.com/2016/01/real-time-streaming-with-spring-xd-apache-geode-gemfire-and-greenplum-earthquake-data-demo/

Data Orchestration using Hortonworks DataFlow (HDF)
Hortonworks Dataflow (HDF), powered by Apache NiFi, is the first integrated platform that solves the real time complexity and challenges of collecting and transporting data from a multitude of sources be they big or small, fast or slow, always connected or intermittently available
http://zdatainc.com/2016/02/hello-nifi-data-orchestration-using-hortonworks-dataflow-hdf/

Happy Learning!

Data Infrastructure, Data Pipeline and Analytics – Useful Links – September, 2016

Some of the Interesting links i have read in the last couple of weeks around Data, In-Memory Databases, Pipeline Development and Analytics.
In Search of Database Nirvana
An excellent post providing an in-depth look at the possibilities and the challenges for companies that long for a single query engine to rule them all.
https://www.oreilly.com/ideas/in-search-of-database-nirvana
http://www.slideshare.net/RohitJain0813/in-search-of-database-nirvana-the-challenges-of-delivering-hybrid-transactionanalytical-processing
Aerospike Vs Cassandra Comparison
Comparison on Aerospike with Apache Cassandra. Cassandra is a columnar NoSQL database that is great for ingesting and analyzing hundreds of terabytes of data stored on rotational disks. Aerospike is an in-memory, NoSQL database, a key-value store that can run purely in RAM and is also optimized for storing data in Flash (SSDs).
http://www.aerospike.com/when-to-use-aerospike-vs-cassandra/
An overview of Apache Streaming Technologies
A very good comparison comparing technologies around simple event processors, stream processors, and complex event processors.
https://databaseline.wordpress.com/2016/03/12/an-overview-of-apache-streaming-technologies/
Flow based Programming
Flow-Based Programming defines applications using the metaphor of a “data factory”. It views an application not as a single, sequential process, which starts at a point in time, and then does one thing at a time until it is finished, but as a network of asynchronous processes communicating by means of streams of structured data chunks, called “information packets” (IPs).
http://www.jpaulmorrison.com/fbp/introduction.html
Hadoop Deployment Cheat Sheet
If you are using, or planning to use the Hadoop framework for big data and Business Intelligence (BI) this document can help you navigate some of the technology and terminology, and guide you in setting up and configuring the system.
http://jethro.io/hadoop-deployment-cheat-sheet/
Amazon Redshift for Custom Analytics
Experience summary on building Custom Analytics on top of Redshift
https://www.alooma.com/blog/custom-analytics-amazon-redshift
Building Analytics at 500px
Experience summary on how they have built the ecosystem
https://medium.com/@samson_hu/building-analytics-at-500px-92e9a7005c83#.pdkk7xrui
How Artificial Intelligence Will Kickstart the Internet of Things?
IoT will produce a tsunami of big data, with the rapid expansion of devices and sensors connected to the Internet of Things continues, the sheer volume of data being created by them will increase to an astronomical level. This data will hold extremely valuable insights into what’s working well or what’s not.
https://datafloq.com/read/Artificial-Intelligence-Kickstart-Internet-Things/1776

Happy Learning!