Awesome Banking API's
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The document discusses the integration of various technologies like Hadoop, Cassandra, and Akka to create banking applications that utilize big data and streaming analytics. It emphasizes the importance of effective data handling, machine learning libraries, and the development of APIs for real-time analytics. Key lessons include the benefits of technology mixing and the need for clear interfaces and team integration.
![Sprayin’
trait ApiService extends HttpService {
// Create Analytics client actor
val actor = actorRefFactory.actorOf(Props[AnalyticsActor], "analytics-actor")
//curl -vv -H "Content-Type: application/json" localhost:8888/api/v1/123/567
val serviceRoute = {
pathPrefix("api" / "v1") {
pathPrefix( Segment / Segment ) {
(aid, cid) =>
get {
complete {
actor ? (aid, cid)
Create an actor
for analytics
Serve the
API path
Message is passed on to
the analytics actor
https://github.com/natalinobusa/wavr](https://image.slidesharecdn.com/v1awesomebankingapis1-140505103432-phpapp02/85/Awesome-Banking-API-s-21-320.jpg)
Awesome Banking API's
- 1. Awesome Banking APIs Exposing bigdata and streaming analytics using hadoop, cassandra, akka and spray
- 5. The bank statements How I read the bank bills
- 6. The bank statements How I read the bank bills What happened those days
- 7. data is the fabric of our lives
- 8. Personal history: Long term Interaction: Real time events:
- 10. >>> from sklearn.datasets import load_iris >>> from sklearn import tree >>> iris = load_iris() >>> clf = tree.DecisionTreeClassifier() >>> clf = clf.fit(iris.data, iris.target) ● Flexible, coincise language ● Quick to code and prototype ● Portable, visualization libraries Machine learning libraries: scipy, statsmodels, sklearn, matplotlib, ipython Web libraries flask, tornado, (no)SQL clients
- 11. # Multiple Linear Regression Example fit <- lm(y ~ x1 + x2 + x3, data=mydata) summary(fit) # show results ● Language for statitics ● Easy to Analyze and shape data ● Advanced statistical package ● Fueled by academia and professionals ● Very clean visualization packages Packages for machine learning time serie forecasting, clustering, classification decision trees, neural networks Remote procedure calls (RPC) From scala/java via RProcess and Rserve
- 12. OK, let’s build some banking apps
- 13. core banking systems SOAP services and DBs System BUS customer facing appls channels Bank schematic
- 14. Challenges
- 15. Higher separation ! Bigger and Faster Less silos Interactions with core systems
- 17. Reliable Low cost ↓ ↑ Computing Powerhouse
- 18. Reliable Low latency Tunable CAP Data model: hashed rows, sorted wide columns Architecture model: No SPOF, ring of nodes, omogeneous system
- 19. Actor A Actor B Actor C msg 1 msg 2 msg 3 msg 4 ● ● ● ●
- 21. Sprayin’ trait ApiService extends HttpService { // Create Analytics client actor val actor = actorRefFactory.actorOf(Props[AnalyticsActor], "analytics-actor") //curl -vv -H "Content-Type: application/json" localhost:8888/api/v1/123/567 val serviceRoute = { pathPrefix("api" / "v1") { pathPrefix( Segment / Segment ) { (aid, cid) => get { complete { actor ? (aid, cid) Create an actor for analytics Serve the API path Message is passed on to the analytics actor https://github.com/natalinobusa/wavr
- 24. Science & Engineering Statistics, Data Science Python R Visualization IT Infra Big Data Java Scala SQL Hadoop: Big Data Infrastructure, Data Science on large datasets Big Data and Fast Data requires different profiles to be able to achieve the best results
- 25. Some lessons learned ● Mix and match technologies is a good thing ● Harden the design as you go ● Define clear interfaces ● Ease integration among teams ● Hadoop , Cassandra, and Akka: they work! ● Plugin the Data Science !