A real-time architecture using
Hadoop and Storm.
Speaker

Nathan Bijnens
@nathan_gs

A real-time architecture using Hadoop & Storm. #JaxLondon

2
Our Vision

Volume
Big Data

test

A real-time architecture using Hadoop & Storm. #JaxLondon

3
Big Data

Velocity
test

A real-time architecture using Hadoop & Storm. #JaxLondon

4
Our Vision

Volume

test

Variety
A real-time architecture using Hadoop & Storm. #JaxLondon

5
Computing Trends
Current

Past
Computation (CPUs)
Expensive

Computation Cheap
(Many Core Computers)

Disk Storage Expensi...
Credits
Nathan Marz
Ex-Backtype & Twitter
Startup in Stealthmode
Storm
Cascalog
ElephantDB

manning.com/marz

A real-time ...
A Data System

A real-time architecture using Hadoop & Storm. #JaxLondon

8
Data is more than Information

Not all information is equal.
Some information is derived from other pieces of
information....
Data is more than Information

Eventually you will reach the most
This is the information you hold true, simple because it...
Events - Before

Events used to manipulate the
master data.

A real-time architecture using Hadoop & Storm. #JaxLondon

11
Events - After

Today, events are the master
data.

A real-time architecture using Hadoop & Storm. #JaxLondon

12
Data System

everything.

A real-time architecture using Hadoop & Storm. #JaxLondon

13
Events

Data is Immutable

A real-time architecture using Hadoop & Storm. #JaxLondon

14
Events

Data is Time Based

A real-time architecture using Hadoop & Storm. #JaxLondon

15
Capturing change traditionally

Person

Location

Person

Location

Nathan

Antwerp

Nathan

Ghent

Geert

Dendermonde

Ge...
Capturing change

Person

Location

Timestamp

Person

Location

Time

Nathan

Antwerp

2005-01-01

Nathan

Antwerp

2005-...
Query

The data you query is often transformed,
aggregated, ...

A real-time architecture using Hadoop & Storm. #JaxLondon...
Query

Query = function ( all data )

A real-time architecture using Hadoop & Storm. #JaxLondon

19
Number of people living in each city.

Person

Location

Time

Location

Count

Nathan

Antwerp

2005-01-01

Ghent

2

Gee...
Query

All Data

Query

A real-time architecture using Hadoop & Storm. #JaxLondon

22
Query: Precompute

All Data

Precomputed
View

Query

A real-time architecture using Hadoop & Storm. #JaxLondon

23
Layered Architecture

Batch Layer

Speed Layer

Serving Layer

A real-time architecture using Hadoop & Storm. #JaxLondon

...
Layered Architecture

Query

Cassandra

Incoming Data
Hadoop

Elephant
DB

A real-time architecture using Hadoop & Storm. ...
Batch Layer

A real-time architecture using Hadoop & Storm. #JaxLondon

26
Batch Layer

Incoming Data
Hadoop

Elephant
DB

A real-time architecture using Hadoop & Storm. #JaxLondon

27
Batch Layer

Unrestrained computation.

A real-time architecture using Hadoop & Storm. #JaxLondon

28
Batch Layer

No need to De-Normalize.

A real-time architecture using Hadoop & Storm. #JaxLondon

29
Batch Layer

Horizontal scalable.

A real-time architecture using Hadoop & Storm. #JaxLondon

30
Batch Layer

High Latency.
matter.

A real-time architecture using Hadoop & Storm. #JaxLondon

31
Batch Layer

Functional computation, based on
immutable inputs, is idempotent.

A real-time architecture using Hadoop & St...
Batch Layer

Stores master copy of data set...
append only.

A real-time architecture using Hadoop & Storm. #JaxLondon

33
Batch Layer

A real-time architecture using Hadoop & Storm. #JaxLondon

34
Batch: View generation

View #1

Master Dataset

MapReduce

View #2

View #3

A real-time architecture using Hadoop & Stor...
MapReduce

MAP

1. Take a large data set and divide it into subsets
…

2. Perform the same function on all subsets

REDUCE...
Serialization & Schema

Catch errors as quickly as they happen.
Validation on write vs on read.

A real-time architecture ...
Serialization & Schema

CSV is actually a serialization language that is just
poorly defined.

A real-time architecture us...
Serialization & Schema
Use a format with a schema.
-

Thrift
Avro
Protobuffers

A real-time architecture using Hadoop & St...
Batch View Database

Read only database.
No random writes required.

A real-time architecture using Hadoop & Storm. #JaxLo...
Batch View Database

Every iteration produces the
Views from scratch.

A real-time architecture using Hadoop & Storm. #Jax...
Batch View Database
ElephantDB
Splout
Voldemort

A real-time architecture using Hadoop & Storm. #JaxLondon

42
Batch Layer

Just a few hours of data.

Data absorbed into Batch Views

Not yet
absorbed.

A real-time architecture using ...
Speed Layer

A real-time architecture using Hadoop & Storm. #JaxLondon

45
Overview
Cassandra

Incoming Data
Hadoop

Elephant
DB

A real-time architecture using Hadoop & Storm. #JaxLondon

46
Speed Layer

Stream processing.

A real-time architecture using Hadoop & Storm. #JaxLondon

47
Speed Layer

Continuous computation.

A real-time architecture using Hadoop & Storm. #JaxLondon

48
Speed Layer

Transactional.

A real-time architecture using Hadoop & Storm. #JaxLondon

49
Speed Layer

Storing a limited window of data.
Compensating for the last few hours of data.

A real-time architecture usin...
Speed Layer

All the complexity is isolated in the Speed
layer.
-corrected.

A real-time architecture using Hadoop & Storm...
CAP
You have a choice between:
Availability
-

Queries are eventual consistent.

Consistency
-

Queries are consistent.

A...
Eventual accuracy

Some algorithms are hard to implement
in real time. For those cases we could
estimate the results.

A r...
Speed Layer

Real
Time
View 1

Incoming Data
Real
Time
View 2

A real-time architecture using Hadoop & Storm. #JaxLondon

...
Storm
Message passing.
Distributed processing.
Horizontally scalable.
Incremental algorithms.
Fast.
Data in motion.

A rea...
Storm

Nimbus
Supervisor

Supervisor

Executer

Executer

Worker Node

Supervisor
Executer

Executer

Executer

Executer

...
Storm
Tuple

Stream

A real-time architecture using Hadoop & Storm. #JaxLondon

57
Storm
Spout

Bolt

A real-time architecture using Hadoop & Storm. #JaxLondon

58
Storm
Grouping

A real-time architecture using Hadoop & Storm. #JaxLondon

59
Data Ingestion
Kafka
Flume
Scribe
*MQ
Kestrel

A real-time architecture using Hadoop & Storm. #JaxLondon

60
Speed Layer Views
The views are stored in Read & Write database.
-

Cassandra
Hbase
Redis
MySQL
ElasticSearch

Much more c...
Serving Layer

A real-time architecture using Hadoop & Storm. #JaxLondon

62
Overview

Query

Cassandra

Incoming Data
Hadoop

Elephant
DB

A real-time architecture using Hadoop & Storm. #JaxLondon

...
Serving Layer

Random reads

A real-time architecture using Hadoop & Storm. #JaxLondon

64
Serving Layer

This layer queries the Batch & Real Time
views and merges it.

A real-time architecture using Hadoop & Stor...
Serving Layer

Batch
Views

Merge
Real
Time
Views

A real-time architecture using Hadoop & Storm. #JaxLondon

66
Serving Layer

How to query an Average?

A real-time architecture using Hadoop & Storm. #JaxLondon

67
Overview

A real-time architecture using Hadoop & Storm. #JaxLondon

68
Overview

Query

Cassandra

Incoming Data
Hadoop

Elephant
DB

A real-time architecture using Hadoop & Storm. #JaxLondon

...
Lambda Architecture

A real-time architecture using Hadoop & Storm. #JaxLondon

70
Lambda Architecture

Can discard any view, batch and real time,
and just recreate everything from the master
data.

A real...
Lambda Architecture

Mistakes are corrected via recomputation.
Write bad data? Remove the data & recompute.
Bug in view ge...
Lambda Architecture

Data storage is highly optimized.

A real-time architecture using Hadoop & Storm. #JaxLondon

73
Lambda Architecture

Immutability changes everything.

A real-time architecture using Hadoop & Storm. #JaxLondon

74
Questions?

Questions?
@nathan_gs & #BigDataCon13

A real-time architecture using Hadoop & Storm. #JaxLondon

75
DataCrunchers
We enable companies in envisioning, defining and
implementing a data strategy.
A one-stop-shop for all your ...
Thank you

Thank you
@nathan_gs

A real-time architecture using Hadoop & Storm. #JaxLondon

77
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  • 1
  • 2
  • How much data doyou have?
    44 times as much data in the next decade, 15Zbin 2015
    Data silos (erp,crm, …)
    Customers
    Trimble (3Tb inhundatabasesysteem)
    Truvo (wijzigenvaneenindexduurt24u)
    Traditionele systemen kunnen dit volume niet aan.
    How many data do you have?
    Turn 12 terabytes of Tweets created each day into improved product sentiment analysis
    Convert 350 billion annual meter readings to better predict power consumption
    3
  • Real time
    Timesensitivedecisiontaking
    Frauddetection
    Energyallocation
    Marketingcampaigns
    Market transactions
    Solution:
    Real-time solutions in combination with batch (hadoop)
    Nosqlsystems
    4
  • Structured
    Unstructured
    80% is unstructured data,
    A key drawback of using traditional relational database systems is that they're not good at handling variable data.
    Aflexibledata model
    Word, email,foto, text, video, APIs, …?
    What are your needs regarding variety?
    The endresult:bringingstructureintounstructureddata
    Monitor 100’s of live video feeds from surveillance cameras to target points of interest
    Exploit the 80% data growth in images, video and documents to improve customer satisfaction
    5
  • We can afford to keepImmutableCopiesof lots of data.
    We NEED immutability to Coordinate with fewer challenges.
    Semaphores & Locks are the things to avoid:
    Instruction opportunities lost waiting for a semaphore increase with more cores…
    6
  • The #of followers on Twitter = all follows & unfollows combined.
    Account balance
    9
  • Data = event
    In an ever changingworld we found a ‘safe heaven’ for data
    Everything we do generates events:
    Pay with Credit Card
    Commit to Git
    Click on a webpage
    Tweet
    10
  • It is easier tostore all data in a cost effective way.
    Compare to DWH world.
    13
  • Immutability greatly restricts the range of errors that can cause data loss or data corruption.
    Ex.
    Only CR, no moreCRUD.
    Informationmight of course change.
    Fault Tolerance
    Data loss
    Human error, Hardware failure
    Data Corruption
    Parallel metfunctioneelprogrammeren.
    14
  • Allows state regeneration.Eg. What was my bank balance on 1 may 2005?
    15
  • Queries as pure functions that take all data as input is the most general formulation.
    Different functions may look at different portions and aggregate information in different ways.
    19
  • 22
  • Tooslow; might be petabyte scale
    Impala/Drill: why not
    23
  • The batch layer can calculate anything (given enough time).
    28
  • The batchlayer stores the data normalized, but in the views it generates, data is often, if not always de normalized.
    29
  • Not vertically
    30
  • 31
  • It’s OK to croak and restart
    32
  • Is something really immutable when it’s name can change.
    33
  • Doesn’t have to be Hadoop.The importance here is a Distributed FS combined with a processing framework.
    Spark,
    34
  • 35
  • Source: PolybasePass2012.pptx
    http://whyjava.wordpress.com/2011/08/04/how-i-explained-mapreduce-to-my-wife/
    36
  • http://www.quora.com/Apache-Hadoop/What-is-the-advantage-of-writing-custom-input-format-and-writable-versus-the-TextInputFormat-and-Text-writable/answer/Eric-Sammer?srid=PU&st=ns
    Value of schemas
    • Structural integrity
    • Guarantees on what can and can’t be stored
    • Prevents corruption
    Otherwise you’ll detect corruption issues at read-time
    37
  • http://www.quora.com/Apache-Hadoop/What-is-the-advantage-of-writing-custom-input-format-and-writable-versus-the-TextInputFormat-and-Text-writable/answer/Eric-Sammer?srid=PU&st=ns
    38
  • 39
  • 40
  • 41
  • Maarkanopgelostworden, doorbvbES je views opvoorhandtegenereren.
    42
  • 43
  • 47
  • 48
  • In some circumstances.
    49
  • 50
  • All the complexity of *dealing* with the CAP theorem (like read repair) is isolated in the realtime layer.
    51
  • Consistency (all nodes see the same data at the same time)
    Availability (a guarantee that every request receives a response about whether it was successful or failed)
    Partition tolerance (the system continues to operate despite arbitrary message loss or failure of part of the system)
    http://codahale.com/you-cant-sacrifice-partition-tolerance/
    HbasavsCassandra
    52
  • Eg. Unique counts
    ML
    53
  • 54
  • Nimbus:
    Manages the cluster
    Worker Node:
    Supervisor:
    Manages workers; restartsthem if needed
    Executer
    Physical JVM process.
    Execute tasks (those are spread evenly across the workers)
    Tasks
    Each in his own Thread.
    Is the actual Bolt or Spout.
    Processes the stream.
    56
  • Tuple:
    Named list of values
    Dynamiclytyped
    Stream
    Sequence of Tuples
    57
  • Spout
    Source of Streams
    Sometimesreplayable
    Bolt
    Streamtransformations
    At least 1 input stream
    0 - * output streams
    58
  • 60
  • 61
  • The serving layer needs to be able to answer any query in a short amount of time.
    64
  • 65
  • AVG = sum + count;preaggregate, but not everything is possible.
    67
  • Lambda firstnamed by Alonzo Church, he needed a letter for functional abstraction in theory of computation in the 1930s.
    70
  • Hightolerance for human & system errors.
    71
  • http://www.quora.com/Apache-Hadoop/What-is-the-advantage-of-writing-custom-input-format-and-writable-versus-the-TextInputFormat-and-Text-writable/answer/Eric-Sammer?srid=PU&st=ns
    72
  • Data storage layer optimized independently from query resolution layer
    73
  • If you remember one thing about this presentation is: Immutability.
    74
  • A real-time architecture using Hadoop and Storm @ JAX London

    1. 1. A real-time architecture using Hadoop and Storm.
    2. 2. Speaker Nathan Bijnens @nathan_gs A real-time architecture using Hadoop & Storm. #JaxLondon 2
    3. 3. Our Vision Volume Big Data test A real-time architecture using Hadoop & Storm. #JaxLondon 3
    4. 4. Big Data Velocity test A real-time architecture using Hadoop & Storm. #JaxLondon 4
    5. 5. Our Vision Volume test Variety A real-time architecture using Hadoop & Storm. #JaxLondon 5
    6. 6. Computing Trends Current Past Computation (CPUs) Expensive Computation Cheap (Many Core Computers) Disk Storage Expensive Disk Storage Cheap (Cheap Commodity Disks) DRAM Expensive DRAM / SSD Getting Cheap Coordination Easy (Latches Don t Often Hit) Coordination Hard (Latches Stall a Lot, etc) Source: Immutability Changes Everything - Pat Helland, RICON2012 A real-time architecture using Hadoop & Storm. #JaxLondon 6
    7. 7. Credits Nathan Marz Ex-Backtype & Twitter Startup in Stealthmode Storm Cascalog ElephantDB manning.com/marz A real-time architecture using Hadoop & Storm. #JaxLondon 7
    8. 8. A Data System A real-time architecture using Hadoop & Storm. #JaxLondon 8
    9. 9. Data is more than Information Not all information is equal. Some information is derived from other pieces of information. A real-time architecture using Hadoop & Storm. #JaxLondon 9
    10. 10. Data is more than Information Eventually you will reach the most This is the information you hold true, simple because it exists. A real-time architecture using Hadoop & Storm. #JaxLondon 10
    11. 11. Events - Before Events used to manipulate the master data. A real-time architecture using Hadoop & Storm. #JaxLondon 11
    12. 12. Events - After Today, events are the master data. A real-time architecture using Hadoop & Storm. #JaxLondon 12
    13. 13. Data System everything. A real-time architecture using Hadoop & Storm. #JaxLondon 13
    14. 14. Events Data is Immutable A real-time architecture using Hadoop & Storm. #JaxLondon 14
    15. 15. Events Data is Time Based A real-time architecture using Hadoop & Storm. #JaxLondon 15
    16. 16. Capturing change traditionally Person Location Person Location Nathan Antwerp Nathan Ghent Geert Dendermonde Geert Dendermonde John Ghent John Ghent A real-time architecture using Hadoop & Storm. #JaxLondon 16
    17. 17. Capturing change Person Location Timestamp Person Location Time Nathan Antwerp 2005-01-01 Nathan Antwerp 2005-01-01 Geert Dendermonde 2011-10-08 Geert Dendermonde 2011-10-08 John Ghent 2010-05-02 John Ghent 2010-05-02 Nathan Ghent 2013-02-03 A real-time architecture using Hadoop & Storm. #JaxLondon 17
    18. 18. Query The data you query is often transformed, aggregated, ... A real-time architecture using Hadoop & Storm. #JaxLondon 18
    19. 19. Query Query = function ( all data ) A real-time architecture using Hadoop & Storm. #JaxLondon 19
    20. 20. Number of people living in each city. Person Location Time Location Count Nathan Antwerp 2005-01-01 Ghent 2 Geert Dendermonde 2011-10-08 Dendermonde 1 John Ghent 2010-05-02 Nathan Ghent 2013-02-03 A real-time architecture using Hadoop & Storm. #JaxLondon 20
    21. 21. Query All Data Query A real-time architecture using Hadoop & Storm. #JaxLondon 22
    22. 22. Query: Precompute All Data Precomputed View Query A real-time architecture using Hadoop & Storm. #JaxLondon 23
    23. 23. Layered Architecture Batch Layer Speed Layer Serving Layer A real-time architecture using Hadoop & Storm. #JaxLondon 24
    24. 24. Layered Architecture Query Cassandra Incoming Data Hadoop Elephant DB A real-time architecture using Hadoop & Storm. #JaxLondon 25
    25. 25. Batch Layer A real-time architecture using Hadoop & Storm. #JaxLondon 26
    26. 26. Batch Layer Incoming Data Hadoop Elephant DB A real-time architecture using Hadoop & Storm. #JaxLondon 27
    27. 27. Batch Layer Unrestrained computation. A real-time architecture using Hadoop & Storm. #JaxLondon 28
    28. 28. Batch Layer No need to De-Normalize. A real-time architecture using Hadoop & Storm. #JaxLondon 29
    29. 29. Batch Layer Horizontal scalable. A real-time architecture using Hadoop & Storm. #JaxLondon 30
    30. 30. Batch Layer High Latency. matter. A real-time architecture using Hadoop & Storm. #JaxLondon 31
    31. 31. Batch Layer Functional computation, based on immutable inputs, is idempotent. A real-time architecture using Hadoop & Storm. #JaxLondon 32
    32. 32. Batch Layer Stores master copy of data set... append only. A real-time architecture using Hadoop & Storm. #JaxLondon 33
    33. 33. Batch Layer A real-time architecture using Hadoop & Storm. #JaxLondon 34
    34. 34. Batch: View generation View #1 Master Dataset MapReduce View #2 View #3 A real-time architecture using Hadoop & Storm. #JaxLondon 35
    35. 35. MapReduce MAP 1. Take a large data set and divide it into subsets … 2. Perform the same function on all subsets REDUCE DoWork() DoWork() DoWork() … 3. Combine the output from all subsets … Output A real-time architecture using Hadoop & Storm. #JaxLondon 36
    36. 36. Serialization & Schema Catch errors as quickly as they happen. Validation on write vs on read. A real-time architecture using Hadoop & Storm. #JaxLondon 37
    37. 37. Serialization & Schema CSV is actually a serialization language that is just poorly defined. A real-time architecture using Hadoop & Storm. #JaxLondon 38
    38. 38. Serialization & Schema Use a format with a schema. - Thrift Avro Protobuffers A real-time architecture using Hadoop & Storm. #JaxLondon 39
    39. 39. Batch View Database Read only database. No random writes required. A real-time architecture using Hadoop & Storm. #JaxLondon 40
    40. 40. Batch View Database Every iteration produces the Views from scratch. A real-time architecture using Hadoop & Storm. #JaxLondon 41
    41. 41. Batch View Database ElephantDB Splout Voldemort A real-time architecture using Hadoop & Storm. #JaxLondon 42
    42. 42. Batch Layer Just a few hours of data. Data absorbed into Batch Views Not yet absorbed. A real-time architecture using Hadoop & Storm. #JaxLondon Now Time 44
    43. 43. Speed Layer A real-time architecture using Hadoop & Storm. #JaxLondon 45
    44. 44. Overview Cassandra Incoming Data Hadoop Elephant DB A real-time architecture using Hadoop & Storm. #JaxLondon 46
    45. 45. Speed Layer Stream processing. A real-time architecture using Hadoop & Storm. #JaxLondon 47
    46. 46. Speed Layer Continuous computation. A real-time architecture using Hadoop & Storm. #JaxLondon 48
    47. 47. Speed Layer Transactional. A real-time architecture using Hadoop & Storm. #JaxLondon 49
    48. 48. Speed Layer Storing a limited window of data. Compensating for the last few hours of data. A real-time architecture using Hadoop & Storm. #JaxLondon 50
    49. 49. Speed Layer All the complexity is isolated in the Speed layer. -corrected. A real-time architecture using Hadoop & Storm. #JaxLondon 51
    50. 50. CAP You have a choice between: Availability - Queries are eventual consistent. Consistency - Queries are consistent. A real-time architecture using Hadoop & Storm. #JaxLondon 52
    51. 51. Eventual accuracy Some algorithms are hard to implement in real time. For those cases we could estimate the results. A real-time architecture using Hadoop & Storm. #JaxLondon 53
    52. 52. Speed Layer Real Time View 1 Incoming Data Real Time View 2 A real-time architecture using Hadoop & Storm. #JaxLondon 54
    53. 53. Storm Message passing. Distributed processing. Horizontally scalable. Incremental algorithms. Fast. Data in motion. A real-time architecture using Hadoop & Storm. #JaxLondon 55
    54. 54. Storm Nimbus Supervisor Supervisor Executer Executer Worker Node Supervisor Executer Executer Executer Executer Executer Executer Executer Worker Node Zookeeper Worker Node A real-time architecture using Hadoop & Storm. #JaxLondon 56
    55. 55. Storm Tuple Stream A real-time architecture using Hadoop & Storm. #JaxLondon 57
    56. 56. Storm Spout Bolt A real-time architecture using Hadoop & Storm. #JaxLondon 58
    57. 57. Storm Grouping A real-time architecture using Hadoop & Storm. #JaxLondon 59
    58. 58. Data Ingestion Kafka Flume Scribe *MQ Kestrel A real-time architecture using Hadoop & Storm. #JaxLondon 60
    59. 59. Speed Layer Views The views are stored in Read & Write database. - Cassandra Hbase Redis MySQL ElasticSearch Much more complex than a read only view. A real-time architecture using Hadoop & Storm. #JaxLondon 61
    60. 60. Serving Layer A real-time architecture using Hadoop & Storm. #JaxLondon 62
    61. 61. Overview Query Cassandra Incoming Data Hadoop Elephant DB A real-time architecture using Hadoop & Storm. #JaxLondon 63
    62. 62. Serving Layer Random reads A real-time architecture using Hadoop & Storm. #JaxLondon 64
    63. 63. Serving Layer This layer queries the Batch & Real Time views and merges it. A real-time architecture using Hadoop & Storm. #JaxLondon 65
    64. 64. Serving Layer Batch Views Merge Real Time Views A real-time architecture using Hadoop & Storm. #JaxLondon 66
    65. 65. Serving Layer How to query an Average? A real-time architecture using Hadoop & Storm. #JaxLondon 67
    66. 66. Overview A real-time architecture using Hadoop & Storm. #JaxLondon 68
    67. 67. Overview Query Cassandra Incoming Data Hadoop Elephant DB A real-time architecture using Hadoop & Storm. #JaxLondon 69
    68. 68. Lambda Architecture A real-time architecture using Hadoop & Storm. #JaxLondon 70
    69. 69. Lambda Architecture Can discard any view, batch and real time, and just recreate everything from the master data. A real-time architecture using Hadoop & Storm. #JaxLondon 71
    70. 70. Lambda Architecture Mistakes are corrected via recomputation. Write bad data? Remove the data & recompute. Bug in view generation? Just recompute the view. A real-time architecture using Hadoop & Storm. #JaxLondon 72
    71. 71. Lambda Architecture Data storage is highly optimized. A real-time architecture using Hadoop & Storm. #JaxLondon 73
    72. 72. Lambda Architecture Immutability changes everything. A real-time architecture using Hadoop & Storm. #JaxLondon 74
    73. 73. Questions? Questions? @nathan_gs & #BigDataCon13 A real-time architecture using Hadoop & Storm. #JaxLondon 75
    74. 74. DataCrunchers We enable companies in envisioning, defining and implementing a data strategy. A one-stop-shop for all your Big Data needs. The first Big Data Consultancy agency in Belgium. A real-time architecture using Hadoop & Storm. #JaxLondon 76
    75. 75. Thank you Thank you @nathan_gs A real-time architecture using Hadoop & Storm. #JaxLondon 77
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