Top 5 Big Data Platform predictions for 2017

December 21, 2016 Leave a comment

The Rise of Data Science Notebooks

Apache Zeppelin is a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala or Python and more. However Apache Zeppelin is still an incubator project, I expect a serious boost of notebooks like Apache Zeppelin on top of data processing (like Apache Spark) and data storage (like HDFS, NoSQL and also RDBMS) solutions. Read more on my previous post.

Splice Machine replace traditional RDBMSs

Splice Machine delivers an open-source database solution that incorporates the proven scalability of Hadoop, the standard ANSI SQL and ACID transactions of an RDBMS, and the in-memory performance of Apache Spark.


Machine Learning as a Service (MLaaS)

Machine Learning is the subfield of computer science that “gives computers the ability to learn without being explicitly programmed”. Within the field of data analytics, Machine Learning is a method used to devise complex models and algorithms that lend themselves to prediction; this is known as predictive analytics. The rise of Machine Learning as a Service (MLaaS) model is good news for the market, because it reduce the complexity and time required to implement Machine Learning and opens the doors to increase the adoption level. One of the companies that provide MLaaS is Microsoft with Azure ML.

Apache Spark on Kubernetes with Red Hat OpenShift

OpenShift is Red Hat‘s Platform-as-a-Service (PaaS) that allows developers to quickly develop, host, and scale applications in a cloud environment. OpenShift is a perfect platform for building data-driven applications with microservices. Apache Spark can be made natively aware of Kubernetes with OpenShift by implementing a Spark scheduler backend that can run Spark application Drivers and bare Executors in Kubernetes pods. See more on the OpenShift Commons Big Data SIG #2 blog.

MapR-FS instead of HDFS

If you’re familiar with the HDFS architecture, you’ll know about the NameNode concept, which is a separate server process that handles the locations of files within your clusters. MapR-FS doesn’t have such a concept, because all that information is embedded within all the data nodes, so it’s distributed across the cluster. The second architectural difference is the fact that MapR is written in native code and talks to directly to disk. HDFS (written in Java) runs in the JVM and then talk to a Linux file system before it talks to disks, so you have a few layers there that will impact performance and scalability. Read more differences in the MapR-FS vs. HDFS blog.

Apache Zeppelin “the notebook” on top of all the (Big) Data

November 30, 2016 1 comment

Apache Zeppelin is a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala or Python and more. However Apache Zeppelin is still an incubator project, I expect a serious boost of notebooks like Apache Zeppelin on top of data processing (like Apache Spark) and data storage (like HDFS, NoSQL and also RDBMS) solutions.

But is Apache Zeppelin covered in the current Hadoop distributions Cloudera, Hortonworks and MapR?

Cloudera is not covering Apache Zeppelin out of the box, but there is blog post how to install Apache Zeppelin on CDH. Hortonworks is covering Apache Zeppelin out of the box, see the picture of the HDP projects (well done Hortonworks). MapR is not covering Apache Zeppelin out of the box, but there is a blog post how to build Apache Zeppelin on MapR.




Is Apache Zeppelin covered by the greatest cloud providers Amazon AWS, Microsoft Azure and Google Cloud Platform?

We see that Amazon Web Services (AWS) has a Platform as a Service solution (PaaS) called Elastic Map Reduce (EMR). We see that since this summer Apache Zeppelin is supported on the EMR release page.


If we look at Microsoft Azure, there is a blog post how to start with Apache Zeppelin on the HD Insights Spark Cluster (this is a also a PaaS solution).

If we look at Google Cloud Platform we see a blog post to install Apache Zeppelin on top of Google BigQuery.

And now a short demo, lets do some data discovery with Apache Zeppelin on an open data set. For this case I use the Fire Report from the City of Amsterdam from 2010 – 2015. 

If you want a short intro look first at this short video of Apache Zeppelin (overview).

I use of course docker to start a Zeppelin container. I found an image in the docker hub from Dylan Meissner (thx). Run the docker container to enter the command below:

$ docker run -d -p 8080:8080 dylanmei/zeppelin

Look in the browser at dockerhost:8080 and create a new notebook:

Step 1: Load and unzip the dataset (I use the “shell” interpreter)

wget -O /tmp/
unzip /tmp/ -d /tmp

Step 2: Clean the data, in this case remove the header

sed -i '1d' /tmp/brwaa_2010-2015.csv

Step 3: Put data into HDFS

hadoop fs -put /tmp/brwaa_2010-2015.csv /tmp

Step 4: Load the data (most import fields) via a class and use the map function (default Scala)

val dataset=sc.textFile("/tmp/brwaa_2010-2015.csv")
case class Melding (id:  Integer, melding_type: String, jaar: String, maand_nr: String, prioriteit: String, uur: String, dagdeel: String, buurt: String, wijk: String, gemeente: String)
val melding =>k.split(";")).map(
k => Melding(k(0).toInt,k(2),k(7),k(8),k(14),k(15),k(16),k(19),k(20),k(22))

Step 5: Use Spark SQL to run the first query

select count(*)
from melding_table

Below you can see some more queries and charts:



Next step is how to predict fire with help of Spark ML.

Stream and analyse Tweets with the ELK / Docker stack in 3 simple steps

July 20, 2015 Leave a comment

There are a lot of possibilities with Big Data tools on the today’s market. For example if we want to stream and analyse some tweets there are several ways to do this. For example:

  • With the Hadoop ecosystem Flume, Spark, Hive etc. and present it for example with Oracle Big Data Discovery.
  • With the Microsoft Stack from Azure (Hadoop) and with Power BI to Excel.
  • Or with this blog with the ELK stack (see Elastic) with the help of the Docker ecosystem
  • and of course al lot more solutions …

What is the ELK stack? ELK stands for Elasticsearch (Search & Analyze Data in Real Time), Logstash (Process Any Data, From Any Source) and Kibana (Explore & Visualize Your Data). More in info at Elastic.

Most of you know Docker already. Docker allows you to package an application with all of its dependencies into a standarized unit for software development. And … Docker will be more and more import with all kind of Open Source Big Data solutions.

Let’s go for it!

Here are the 3 simple steps:

  • Step 1: Install and test Elasticsearch
  • Step 2: Install and test Logstash
  • Step 3: Install and test Kibana

For now you need only a dockerhost. I use Docker Toolbox.

If you don’t have a virtual machine, create one. For example:

$ docker-machine create --driver virtualbox --virtualbox-disk-size "40000" dev

Logon to the dockerhost (in my case Docker Toolbox)

$ docker-machine ssh dev

Step 1: Install and test Elasticsearch


docker run --name elasticsearch -p 9200:9200 -d elasticsearch _non_loopback_

The – _non_loopback_ option must be added from elasticsearch 2.0 to handle localhost


curl http://localhost:9200

You must see something like:

  "status" : 200,
  "name" : "Dragonfly",
  "cluster_name" : "elasticsearch",
  "version" : {
    "number" : "1.7.0",
    "build_hash" : "929b9739cae115e73c346cb5f9a6f24ba735a743",
    "build_timestamp" : "2015-07-16T14:31:07Z",
    "build_snapshot" : false,
    "lucene_version" : "4.10.4"
  "tagline" : "You Know, for Search"

Step 2: Install and test Logstash

Make the following “config.conf” file for streaming tweets to elasticsearch. Fill in your twitter credentials and the docker host ip. For this example we search all the tweets with the keyword “elasticsearch”. For now we do not use a filter.

input {
  twitter {
      consumer_key => ""
      consumer_secret => ""
      oauth_token => ""
      oauth_token_secret => ""
      keywords => [ "elasticsearch" ]
      full_tweet => true
filter {
output {
  stdout { codec => dots }
  elasticsearch {
    protocol => "http"
    host => "<Docker Host IP>"
    index => "twitter"
    document_type => "tweet"


docker run --name logstash -it --rm -v "$PWD":/config logstash logstash
 -f /config/config.conf

Test (if everything went well) you see:

Logstash startup completed

Step 3: Install and test Kibana

Install and link with the elasticsearch container:

docker run --name kibana --link elasticsearch:elasticsearch -p 5601:5601 -d kibana


curl http://localhost:5601

You must see something like a html page. Go to a browser and see the Kibana 4 user interface.

Start to go to settings tab and enter the new index name “twitter”

Go to the discover tab and you see something like this (after 60 minutes):

Screen Shot 2015-07-20 at 19.51.14

I also make a pie chart (Tweets per Location) like this:

Screen Shot 2015-07-20 at 19.56.44

And there are a lot of more possibilities with the ELK stack. Enjoy!

More info:

Install single node Hadoop on CentOS 7 in 5 simple steps

August 22, 2014 30 comments

First install CentOS 7 (minimal) (CentOS-7.0-1406-x86_64-DVD.iso)

I have download the CentOS 7 ISO here

### Vagrant Box

You can use my vagrant box voor a default CentOS 7, if you are using virtual box

$ vagrant init malderhout/centos7
$ vagrant up
$ vagrant ssh

### Be aware that you add the hostname “centos7” in the /etc/hosts centos7 localhost localhost.localdomain localhost4 localhost4.localdomain4

### Add port forwarding to the Vagrantfile located on the host machine. for example: “forwarded_port”, guest: 50070, host: 50070

### If not root, start with root

$ sudo su

### Install wget, we use this later to obtain the Hadoop tarball

$ yum install wget

### Disable the firewall (not needed if you use the vagrant box)

$ systemctl stop firewalld


We install Hadoop in 5 simple steps:
1) Install Java
2) Install Hadoop
3) Configurate Hadoop
4) Start Hadoop
5) Test Hadoop

1) Install Java

### install OpenJDK Runtime Environment (Java SE 7)

$ yum install java-1.7.0-openjdk

2) Install Hadoop

### create hadoop user

$ useradd hadoop

### login to hadoop

$ su - hadoop

### generating SSH Key

$ ssh-keygen -t dsa -P '' -f ~/.ssh/id_dsa

### authorize the key

$ cat ~/.ssh/ >> ~/.ssh/authorized_keys

### set chmod

$ chmod 0600 ~/.ssh/authorized_keys

### verify key works / check no password is needed

$ ssh localhost
$ exit

### download and install hadoop tarball from apache in the hadoop $HOME directory

$ wget
$ tar xzf hadoop-2.5.0.tar.gz

3) Configurate Hadoop

### Setup Environment Variables. Add the following lines to the .bashrc

export JAVA_HOME=/usr/lib/jvm/jre
export HADOOP_HOME=/home/hadoop/hadoop-2.5.0

### initiate variables

$ source $HOME/.bashrc

### Put the property info below between the “configuration” tags for each file tags for each file

### Edit $HADOOP_HOME/etc/hadoop/core-site.xml


### Edit $HADOOP_HOME/etc/hadoop/hdfs-site.xml




### copy template

$ cp $HADOOP_HOME/etc/hadoop/mapred-site.xml.template $HADOOP_HOME/etc/hadoop/mapred-site.xml

### Edit $HADOOP_HOME/etc/hadoop/mapred-site.xml


### Edit $HADOOP_HOME/etc/hadoop/yarn-site.xml


### set JAVA_HOME
### Edit $HADOOP_HOME/etc/hadoop/ and add the following line

export JAVA_HOME=/usr/lib/jvm/jre

4) Start Hadoop

# format namenode to keep the metadata related to datanodes

$ hdfs namenode -format

# run script


# check that HDFS is running
# check there are 3 java processes:
# namenode
# secondarynamenode
# datanode


# check there are 2 more java processes:
# resourcemananger
# nodemanager

5) Test Hadoop

### access hadoop via the browser on port 50070

Screen Shot 2014-08-22 at 14.55.27

### put a file

$ hdfs dfs -mkdir /user
$ hdfs dfs -mkdir /user/hadoop
$ hdfs dfs -put /var/log/boot.log

### check in your browser if the file is available

Screen Shot 2014-08-22 at 14.57.53

Works!!! See also


Stream Tweets in MongoDB with Node.JS

July 5, 2013 1 comment

Suppose we want store al our “mongodb” tweets in a MongoDB database.

We need 2 additional node packages:

1) ntwitter (Asynchronous Twitter REST/stream/search client API for Node.js)
2) mongodb (A Node.js driver for MongoDB). Of course there are more MongoDB drivers.

Create a Node.js project “twitterstream” and add the 2 packages with the following commands:

$ npm install ntwitter
$ npm install mongodb

We need an existing twitter account and make a credential file for example credentials.js.

var credentials = {
    consumer_key: '3h7ryXnH209mHNWvTgon5A',
    consumer_secret: 'tD5OdqXw1qbDMrFbrtPIRRl4fEyUsKFXT2kZLQaMpVA',
    access_token_key: '474665342-wuRquALXNQZPYABUiOnXCmVSxyU2LIinV6VwpWMW',
    access_token_secret: 'k01HuXdl8umwt5rZcDDk0OgQJbhkiFlPv2dCAmHXQ'

module.exports = credentials;

And now we create the main file twitter.js with the following code:

var twitter = require('ntwitter');
var credentials = require('./credentials.js');

var t = new twitter({
    consumer_key: credentials.consumer_key,
    consumer_secret: credentials.consumer_secret,
    access_token_key: credentials.access_token_key,
    access_token_secret: credentials.access_token_secret

var mongo = require('mongodb');

var Server = mongo.Server,
    Db = mongo.Db,
    assert = require('assert')
    BSON = mongo.BSONPure;

var server = new Server('localhost', 27017, {auto_reconnect: true});
db = new Db('twitterstream', server);
// open db, db) {
  assert.equal(null, err);
    { track: ['mongodb'] },
    function(stream) {
        stream.on('data', function(tweet) {
            db.collection('streamadams', function(err, collection) {
               collection.insert({'tweet': tweet.text, {safe:true}
                                 , function(err, result) {});

Simply start the twitter.js with:

$ node twitter.js

Succes with Node.JS and MongoDB!

How to Fetch RSS feeds into MongoDB with Groovy

May 20, 2013 1 comment

Suppose we will fetch some Amazon AWS news into a MongoDB database. These few lines made it possible with the use of Groovy and the Gmongo module:

// To download GMongo on the fly and put it at classpath
@Grab(group='com.gmongo', module='gmongo', version='1.0')
import com.gmongo.GMongo

// Instantiate a com.gmongo.GMongo object instead of com.mongodb.Mongo
// The same constructors and methods are available here
def mongo = new GMongo("", 27017)

// Get a db reference
def db = mongo.getDB("amazonnews")

// Give the url of the RSS feed
def url = ""

// Parse the url with famous XML Slurper
def rss = new XmlSlurper().parse(url) 

// Write the title and link into the news collection {[title: "${it.title}", link: "${}"])

Connect to the MongoDB database if there some documents:

> use amazonnews
switched to db amazonnews
{ "_id" : ObjectId("519a2e980364e3901f41827d"), "title" : "Amazon Elastic Transcoder Announces Seven New Enhancements, Including HLS Support", "link" : "" }
{ "_id" : ObjectId("519a2e980364e3901f41827e"), "title" : "Amazon DynamoDB Announces Parallel Scan and Lower-Cost Reads", "link" : "" }
{ "_id" : ObjectId("519a2e990364e3901f41827f"), "title" : "AWS Management Console in AWS GovCloud (US) adds support for Amazon SWF", "link" : "" }
{ "_id" : ObjectId("519a2e990364e3901f418280"), "title" : "AWS OpsWorks launches Amazon CloudWatch metrics view", "link" : "" }
{ "_id" : ObjectId("519a2e990364e3901f418281"), "title" : "AWS OpsWorks supports Elastic Load Balancing", "link" : "" }
{ "_id" : ObjectId("519a2e990364e3901f418282"), "title" : "AWS Direct Connect location in Seattle and access to AWS GovCloud (US) now available", "link" : "" }
{ "_id" : ObjectId("519a2e990364e3901f418283"), "title" : "Announcing AWS Management Pack for Microsoft System Center ", "link" : "" }
{ "_id" : ObjectId("519a2e990364e3901f418284"), "title" : "Raising the bar: Amazon announces 4,000 IOPS per EBS Volume and Provisioned IOPS products on AWS Marketplace", "link" : "" }
{ "_id" : ObjectId("519a2e990364e3901f418285"), "title" : "Announcing General Availability of the AWS SDK for Node.js", "link" : "" }
{ "_id" : ObjectId("519a2e990364e3901f418286"), "title" : "Amazon Elastic MapReduce (EMR) now supports S3 Server Side Encryption", "link" : "" }


More info:

Implement MongoDB replication in 3 simple steps

November 2, 2012 Leave a comment

After we find out how replication works with MySQL lets look at mongoDB

Use the following steps to implement mongoDB Replication:

1) Create the data directories
2) Create the replication set and instances
3) Configure, primary, secundaries and an arbiter

Donwload MongoDB? Goto the Download site

Step 1) Create the data directories

Start by creating a data directory for each replica set member, one for the primary and one for the secundary. We add also an arbiter. The arbiter does not relpicate data, but choose a new primary in case there is an outage of the existing primary.

mkdir /data/node1
mkdir /data/node2
mkdir /data/arbiter

Step 2) Create the replication set and instances

Next, start each member as a separate mongod. Since you’ll be running each process on the same machine, it’s probably easiest to start each mongod in a separate terminal window:

mongod --replSet person --dbpath /data/node1 --port 40001
mongod --replSet person --dbpath /data/node2 --port 40002
mongod --replSet person --dbpath /data/arbiter --port 40003

Step 3) Configure, primary, secundaries and an arbiter

Logon on the primary node to proceed, you need to configure the replica set, because if you examine the mongod log output, the first thing you’ll notice are error messages saying that the configuration can’t be found.

mongo localhost:40001
MongoDB shell version: 2.2.0
connecting to: localhost:40001/test
> rs.initiate()
"info2" : "no configuration explicitly specified -- making one",
"me" : "Computername.local:40001",
"info" : "Config now saved locally. Should come online in about a minute.",
"ok" : 1

Now connect again to the primary node, and add the secondary node including the arbiter node:

person:PRIMARY> rs.add(Computername:40002)
{ "ok" : 1 }
person:PRIMARY> rs.add(Computername:40003, {arbiterOnly:true})
{ "ok" : 1 }

Check if the configuration is ok, with rs.status():

person:PRIMARY> rs.status()
 "set" : "person",
 "date" : ISODate("2012-10-28T19:50:52Z"),
 "myState" : 1,
 "members" : [
 "_id" : 0,
 "name" : "Computername.local:40001",
 "health" : 1,
 "state" : 1,
 "stateStr" : "PRIMARY",
 "uptime" : 1266,
 "optime" : Timestamp(1351453811000, 1),
 "optimeDate" : ISODate("2012-10-28T19:50:11Z"),
 "self" : true
 "_id" : 1,
 "name" : "Computername.local:40002",
 "health" : 1,
 "state" : 2,
 "stateStr" : "SECONDARY",
 "uptime" : 41,
 "optime" : Timestamp(1351453811000, 1),
 "optimeDate" : ISODate("2012-10-28T19:50:11Z"),
 "lastHeartbeat" : ISODate("2012-10-28T19:50:51Z"),
 "pingMs" : 0
 "ok" : 1
 "_id" : 1,
 "name" : "Computername.local:40003",
 "health" : 1,
 "state" : 3,
 "stateStr" : "ARBITER",
 "uptime" : 14,
 "optime" : Timestamp(1351453811000, 1),
 "optimeDate" : ISODate("2012-10-28T19:50:11Z"),
 "lastHeartbeat" : ISODate("2012-10-28T19:50:51Z"),
 "pingMs" : 0
 "ok" : 1

And now its time to check if it works. We put a person in our primary database:

person:PRIMARY> use portraitGallery
switched to db portraitGallery
"name" : "Maikel",
"group" : [ "Oracle", "ExaData", "Big Data"],
} )

Logon on the secondary and check if the data is there, and don’t forget to enable reading with rs.slaveOk() or db.getMongo().setSlaveOk()

mongo localhost:40002
MongoDB shell version: 2.2.0
connecting to: localhost:40002/test
person:SECONDARY> rs.slaveOk()
person:SECONDARY> use portraitGallery
switched to db portraitGallery
person:SECONDARY> db.person.find()
{ "_id" : ObjectId("508d971dda0730903bcbb612"), "name" : "Maikel", "group" : [ "Oracle", "ExaData", "Big Data" ] }

Now we can test it with a filler script. Type in the primary something like:

person:PRIMARY> for(i=0; i<1000000; i++) {{person: i}); }

And in the secondary check if the collection is filled:

person:SECONDARY> db.person.find()
 { "_id" : ObjectId("508f95e9e38917f43ae20db3"), "person" : 0 }
 { "_id" : ObjectId("508f95e9e38917f43ae20db4"), "person" : 1 }
 { "_id" : ObjectId("508f95e9e38917f43ae20db5"), "person" : 2 }
 { "_id" : ObjectId("508f95e9e38917f43ae20db6"), "person" : 3 }
 { "_id" : ObjectId("508f95e9e38917f43ae20db7"), "person" : 4 }
 { "_id" : ObjectId("508f95e9e38917f43ae20db8"), "person" : 5 }
 { "_id" : ObjectId("508f95e9e38917f43ae20db9"), "person" : 6 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dba"), "person" : 7 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dbb"), "person" : 8 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dbc"), "person" : 9 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dbd"), "person" : 10 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dbe"), "person" : 11 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dbf"), "person" : 12 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dc0"), "person" : 13 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dc1"), "person" : 14 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dc2"), "person" : 15 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dc3"), "person" : 16 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dc4"), "person" : 17 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dc5"), "person" : 18 }
 { "_id" : ObjectId("508f95e9e38917f43ae20dc6"), "person" : 19 }
 Type "it" for more
 person:SECONDARY> db.person.count()
 person:SECONDARY> db.person.count()
 person:SECONDARY> db.person.count()
 person:SECONDARY> db.person.count()

Works, succes with mongoDB!!!

If you wan to do the mongoDB intro lab goto

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