Wprowadzenie do technologii Big Data / Intro to Big Data Ecosystem

Wprowadzenie do
technologii Big Data
Radosław Stankiewicz

HackerBD & DS FTW!Technical Lead Trainer
2
Src: computing.co.uk , https://www.ﬂickr.com/photos/barron/15483113 , tech.co

Agenda
Wstęp -> Map Reduce -> Pig -> Hive -> Ambari
4

Variety
7
A|123|10$
B|555|20$
Y|333|15$
{
'typ'='A',
'id'=123,
'kwota'='10$'
}

Velocity
OLAP
Real
Time
Batch
Streaming
Interactive
analytics
8

Przechowywanie danych
pliki (analiza batch i
interaktywna)
NoSQL (random access) Indeksy
pliki płaskie, csv
(rowid,col,czas)->value:
Accumulo, HBase
Cassandra
MongoDB
Solr
Elastic Search
JSON
AVRO
formaty kolumnowe Bazy grafowe
9

Klasyﬁkacja problemu
• Baza danych ulic Warszawy, Dane w formacie JSON,
optymalizacja odbioru śmieci jednego z usługodawców.
• Zdarzenia z bazy transakcyjnej i kart kredytowych w
celu lepszego wykrywania fraudów
• System wyszukujący dobre oferty samochodów z wielu
serwisów - web crawling, parsowanie danych, analiza
trendów cen samochodów
• Centralne repozytorium skanów umów, TB danych,
codziennie przybywa kilkaset nowych dokumentów
12

Geneza
• za dużo danych
• pady serwerów
• wolne relacyjne bazy danych
15

Architektura
19 źródło: Hortonworks

Ekosystem Hadoop
20 źródło: Hortonworks

Wprowadzenie do
MapReduce na przykładzie
platformy Hadoop
21

HDFS
Inspirowany GFS(po prawej)
Główne cechy:
• Fault tolerant
• Commodity, low cost hardware
• Batch processing
• High throughput, not low latency
• Write Once, Read Many
23

HDFS - replikacja
Datanodes
Namenode
25

● User Commands
o dfs
o fsck
● Administration Commands
o datanode
o dfsadmin
o namenode
dfs:
appendToFile cat chgrp chmod chown copyFromLocal copyToLocal count cp du
dus expunge get getfacl getfattr getmerge ls lsr mkdir moveFromLocal
moveToLocal mv put rm rmr setfacl setfattr setrep stat tail test text touchz
hdfs dfs -put localfile1 localfile2 /user/tmp/hadoopdir
hdfs dfs -getmerge /user/hadoop/output/ localfile
komendy
26

Uprawnienia
POSIX - Knox - Ranger
27

Map Reduce Framework
30
M
M
M
M
R
R
R
R
R

Mapper
#!/usr/bin/env python
import sys
for line in sys.stdin:
words = line.strip().split()
for word in words:
print '%st%s' % (word, 1)
line = “Ala ma kota”
Ala 1
ma 1
kota 1
31

Reducer
#!/usr/bin/env python
import sys
current_word = None
current_count = 0
word = None
for line in sys.stdin:
line = line.strip()
word, count = line.split('t', 1)
count = int(count)
if current_word == word:
current_count += count
else:
if current_word:
print '%s,%s' % (current_word, current_count)
current_count = count
current_word = word
if current_word == word:
print '%s,%s' % (current_word, current_count)
ala 1
ala 1
bela 1
dela 1
ala,2
bela,1
dela,1
32

Uruchomienie streaming
cat input.txt | ./mapper.py | sort | ./reducer.py
bin/yarn jar [..]/hadoop-*streaming*.jar  
-ﬁle mapper.py -mapper ./mapper.py -ﬁle
reducer.py -reducer ./reducer.py  
-input /tmp/wordcount/input -output /tmp/
wordcount/output
33

Map Reduce w Java
(input) <k1, v1> -> map -> <k2, v2> -> combine ->
<k2, v2> -> reduce -> <k3, v3> (output)
1) Mapper
2) Reducer
3) run
public class WordCount extends Configured
implements Tool {
public static class TokenizerMapper{...}
public static class IntSumReducer{...}
public int run(...){...}
}
34

Mapper<KEYIN,VALUEIN,KEY
OUT,VALUEOUT>
public static class TokenizerMapper 
extends Mapper<LongWritable, Text, Text, IntWritable>{ 
 
private final static IntWritable one = new IntWritable(1); 
private Text word = new Text(); 
 
public void map(LongWritable key, Text value, Context context 
) throws IOException, InterruptedException { 
StringTokenizer itr = new StringTokenizer(value.toString()); 
while (itr.hasMoreTokens()) { 
word.set(itr.nextToken()); 
context.write(word, one); 
} 
}
public void setup(...) {...}
public void cleanup(...) {...}
public void run(...) {...} 
}
value = “Ala ma kota”
Ala,1
ma,1
kota,1

Reducer<KEYIN,VALUEIN,KEY
OUT,VALUEOUT>
public static class IntSumReducer 
extends Reducer<Text,IntWritable,Text,IntWritable> { 
private IntWritable result = new IntWritable(); 
 
public void reduce(Text key, Iterable<IntWritable> values, 
Context context 
) throws IOException, InterruptedException { 
int sum = 0; 
for (IntWritable val : values) { 
sum += val.get(); 
} 
result.set(sum); 
context.write(key, result); 
}
public void setup(...) {...}
public void cleanup(...) {...}
public void run(...) {...} 
}
kota,(1,1,1,1)
kota,4

Main
public int run(String[] args) throws Exception { 
Configuration conf = new Configuration(); 
Job job = Job.getInstance(conf, "word count"); 
job.setJarByClass(WordCount.class); 
job.setMapperClass(TokenizerMapper.class); 
job.setCombinerClass(IntSumReducer.class); 
job.setReducerClass(IntSumReducer.class); 
job.setOutputKeyClass(Text.class); 
job.setOutputValueClass(IntWritable.class); 
FileInputFormat.addInputPath(job, new Path(args[0])); 
FileOutputFormat.setOutputPath(job, new Path(args[1])); 
System.exit(job.waitForCompletion(true) ? 0 : 1); 
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new WordCount(),args);
System.exit(res);
}
yarn jar wc.jar WordCount /tmp/wordcount/input /tmp/wordcount/output

Co dalej?
• Map Reduce w Javie
• Testowanie MRUnit
• Joins
• Avro
• Custom Key, Value
• Złączanie wielu zadań
• Custom Input, Output
38

Wprowadzenie do
przetwarzania danych na
przykładzie Pig
40

Czy warto?
Top 5 stron odwiedzanych przez
użytkowników mających 18 lat

import java.io.IOException;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.KeyValueTextInputFormat;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.SequenceFileInputFormat;
import org.apache.hadoop.mapred.SequenceFileOutputFormat;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.jobcontrol.Job;
import org.apache.hadoop.mapred.jobcontrol.JobControl;
import org.apache.hadoop.mapred.lib.IdentityMapper;
public class MRExample {
public static class LoadPages extends MapReduceBase
implements Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable k, Text val,
OutputCollector<Text, Text> oc,
Reporter reporter) throws IOException {
// Pull the key out
String line = val.toString();
int firstComma = line.indexOf(',');
String key = line.substring(0, firstComma);
String value = line.substring(firstComma + 1);
Text outKey = new Text(key);
// Prepend an index to the value so we know which file
// it came from.
Text outVal = new Text("1" + value);
oc.collect(outKey, outVal);
}
}
public static class LoadAndFilterUsers extends MapReduceBase
implements Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable k, Text val,
// Pull the key out
String value = line.substring(firstComma + 1);
int age = Integer.parseInt(value);
if (age < 18 || age > 25) return;
String key = line.substring(0, firstComma);
// Prepend an index to the value so we know which file
// it came from.
Text outVal = new Text("2" + value);
oc.collect(outKey, outVal);
}
}
public static class Join extends MapReduceBase
implements Reducer<Text, Text, Text, Text> {
public void reduce(Text key,
Iterator<Text> iter,
// For each value, figure out which file it's from and
store it
// accordingly.
List<String> first = new ArrayList<String>();
List<String> second = new ArrayList<String>();
while (iter.hasNext()) {
Text t = iter.next();
String value = t.toString();
if (value.charAt(0) == '1')
first.add(value.substring(1));
else second.add(value.substring(1));
reporter.setStatus("OK");
}
// Do the cross product and collect the values
for (String s1 : first) {
for (String s2 : second) {
String outval = key + "," + s1 + "," + s2;
oc.collect(null, new Text(outval));
}
}
}

public static class LoadJoined extends MapReduceBase
implements Mapper<Text, Text, Text, LongWritable> {
public void map(
Text k,
Text val,
OutputCollector<Text, LongWritable> oc,
// Find the url
int secondComma = line.indexOf(',', firstComma);
String key = line.substring(firstComma, secondComma);
// drop the rest of the record, I don't need it anymore,
// just pass a 1 for the combiner/reducer to sum instead.
oc.collect(outKey, new LongWritable(1L));
}
}
public static class ReduceUrls extends MapReduceBase
implements Reducer<Text, LongWritable, WritableComparable,
Writable> {
public void reduce(
Text key,
Iterator<LongWritable> iter,
OutputCollector<WritableComparable, Writable> oc,
// Add up all the values we see
long sum = 0;
while (iter.hasNext()) {
sum += iter.next().get();
}
oc.collect(key, new LongWritable(sum));
}
}
public static class LoadClicks extends MapReduceBase
implements Mapper<WritableComparable, Writable, LongWritable,
Text> {
public void map(
WritableComparable key,
Writable val,
OutputCollector<LongWritable, Text> oc,
oc.collect((LongWritable)val, (Text)key);
}
}
public static class LimitClicks extends MapReduceBase
implements Reducer<LongWritable, Text, LongWritable, Text> {
int count = 0;
public void reduce(
LongWritable key,
Iterator<Text> iter,
OutputCollector<LongWritable, Text> oc,
// Only output the first 100 records
while (count < 100 && iter.hasNext()) {
oc.collect(key, iter.next());
count++;
}
}
}

public static void main(String[] args) throws IOException {
JobConf lp = new JobConf(MRExample.class);
lp.setJobName("Load Pages");
lp.setInputFormat(TextInputFormat.class);
lp.setOutputKeyClass(Text.class);
lp.setOutputValueClass(Text.class);
lp.setMapperClass(LoadPages.class);
FileInputFormat.addInputPath(lp, new
Path("/user/gates/pages"));
FileOutputFormat.setOutputPath(lp,
new Path("/user/gates/tmp/indexed_pages"));
lp.setNumReduceTasks(0);
Job loadPages = new Job(lp);
JobConf lfu = new JobConf(MRExample.class);
lfu.setJobName("Load and Filter Users");
lfu.setInputFormat(TextInputFormat.class);
lfu.setOutputKeyClass(Text.class);
lfu.setOutputValueClass(Text.class);
lfu.setMapperClass(LoadAndFilterUsers.class);
FileInputFormat.addInputPath(lfu, new
Path("/user/gates/users"));
FileOutputFormat.setOutputPath(lfu,
new Path("/user/gates/tmp/ﬁltered_users"));
lfu.setNumReduceTasks(0);
Job loadUsers = new Job(lfu);
JobConf join = new JobConf(MRExample.class);
join.setJobName("Join Users and Pages");
join.setInputFormat(KeyValueTextInputFormat.class);
join.setOutputKeyClass(Text.class);
join.setOutputValueClass(Text.class);
join.setMapperClass(IdentityMapper.class);
join.setReducerClass(Join.class);
FileInputFormat.addInputPath(join, new
Path("/user/gates/tmp/indexed_pages"));
FileInputFormat.addInputPath(join, new
Path("/user/gates/tmp/ﬁltered_users"));
FileOutputFormat.setOutputPath(join, new
Path("/user/gates/tmp/joined"));
join.setNumReduceTasks(50);
Job joinJob = new Job(join);
joinJob.addDependingJob(loadPages);
joinJob.addDependingJob(loadUsers);
JobConf group = new JobConf(MRExample.class);
group.setJobName("Group URLs");
group.setInputFormat(KeyValueTextInputFormat.class);
group.setOutputKeyClass(Text.class);
group.setOutputValueClass(LongWritable.class);
group.setOutputFormat(SequenceFileOutputFormat.class);
group.setMapperClass(LoadJoined.class);
group.setCombinerClass(ReduceUrls.class);
group.setReducerClass(ReduceUrls.class);
FileInputFormat.addInputPath(group, new
Path("/user/gates/tmp/joined"));
FileOutputFormat.setOutputPath(group, new
Path("/user/gates/tmp/grouped"));
group.setNumReduceTasks(50);
Job groupJob = new Job(group);
groupJob.addDependingJob(joinJob);
JobConf top100 = new JobConf(MRExample.class);
top100.setJobName("Top 100 sites");
top100.setInputFormat(SequenceFileInputFormat.class);
top100.setOutputKeyClass(LongWritable.class);
top100.setOutputValueClass(Text.class);
top100.setOutputFormat(SequenceFileOutputFormat.class);
top100.setMapperClass(LoadClicks.class);
top100.setCombinerClass(LimitClicks.class);
top100.setReducerClass(LimitClicks.class);
FileInputFormat.addInputPath(top100, new
Path("/user/gates/tmp/grouped"));
FileOutputFormat.setOutputPath(top100, new
Path("/user/gates/top100sitesforusers18to25"));
top100.setNumReduceTasks(1);
Job limit = new Job(top100);
limit.addDependingJob(groupJob);
JobControl jc = new JobControl("Find top 100 sites for users
18 to 25");
jc.addJob(loadPages);
jc.addJob(loadUsers);
jc.addJob(joinJob);
jc.addJob(groupJob);
jc.addJob(limit);
jc.run();
}
}

Users = load ‘users’ as (name, age);
Fltrd = ﬁlter Users by
age >= 18 and age <= 25;
Pages = load ‘pages’ as (user, url);
Jnd = join Fltrd by name, Pages by user;
Grpd = group Jnd by url;
Smmd = foreach Grpd generate group, COUNT(Jnd) as clicks;
Srtd = order Smmd by clicks desc;
Top5 = limit Srtd 5;
store Top5 into ‘top5sites’;

Tryb Pracy
Interaktywny lub Wsadowy
48

Tryb Pracy
Lokalny lub Rozproszony
49

Tryb Pracy
Map Reduce lub Tez
50

Typy danych
51
int long ﬂoat double
chararray datetime boolean
bytearray biginteger bigdecimal

Złożone typy
52
tuple bag map

Podstawy Pig Latin -
wielkość liter
• A = LOAD 'data' USING PigStorage() AS (f1:int, f2:int, f3:int); 
B = GROUP A BY f1; 
C = FOREACH B GENERATE COUNT ($0); 
DUMP C;
• Nazwy zmiennych A, B, and C (tzw. aliasy) są case sensitive.
• Wielkość liter jest też istotna dla:
• nazwy pól f1, f2, i f3
• nazwy zmiennych A, B, C
• nazwy funkcji PigStorage, COUNT
• Z wyjątkiem: LOAD, USING, AS, GROUP, BY, FOREACH, GENERATE, oraz DUMP
53

assert, and, any, all, arrange, as, asc, AVG, bag,
BinStorage, by, bytearray, BIGINTEGER, BIGDECIMAL,
cache, CASE, cat, cd, chararray, cogroup, CONCAT,
copyFromLocal, copyToLocal, COUNT, cp, cross,
datetime, %declare, %default, define, dense, desc,
describe, DIFF, distinct, double, du, dump, e, E,
eval, exec, explain, f, F, filter, flatten, float,
foreach, full, generate, group, help, if, illustrate,
import, inner, input, int, into, is, join, kill, l, L,
left, limit, load, long, ls, map, matches, MAX, MIN,
mkdir, mv, not, null, onschema, or, order, outer,
output, parallel, pig, PigDump, PigStorage, pwd, quit,
register, returns, right, rm, rmf, rollup, run,
sample, set, ship, SIZE, split, stderr, stdin, stdout,
store, stream, SUM, TextLoader, TOKENIZE, through,
tuple, union, using, void
54
Słowa kluczowe

Pierwsze kroki
data = LOAD 'input' AS (query:CHARARRAY);
A = LOAD 'data' USING PigStorage('t') AS (f1:int, f2:int, f3:int);
STORE A INTO '/tmp/result' USING PigStorage(';')
55

Pierwsze kroki
SAMPLE
DESCRIBE
DUMP
EXPLAIN
ILLUSTRATE
56

Kolejne kroki - operacje na
danych
A = LOAD 'student' USING PigStorage() AS (name:chararray, age:int, semestre:int,
scholarship:ﬂoat);
B = FILTER A BY age > 20;
57

danych
C = LIMIT B 5;
58

danych
C = LIMIT B 5;
D = FOREACH C GENERATE name, scholarship*semestre as funds
59

danych
E = GROUP A by age
60

danych
E = GROUP A by age
F = FOREACH E GENERATE group as age, AVG(A.scholarship)
61

Wydajność
Tez, Projekcje, Filtrowanie, Join
62

Co dalej?
UDF, PigUnit, Integracje
63

Wprowadzenie do analizy
danych na przykładzie
Hive
65

Unikalne cechy Hive
Zapytania SQL na plikach płaskich, np. CSV
67

Unikalne cechy Hive
Znaczne przyspieszenie analizy - nie potrzeba pisać Map Reduce
Optymalizacja, wykonywanie części operacji w pamięci zamiast MR
68

Unikalne cechy Hive
Nieograniczone formy integracji - MongoDB, Elastic Search,
HBase, Solr
69

Unikalne cechy Hive
Integracja narzędzi BI i innych aplikacji poprzez JDBC
70

Hive CLI
Tryb Interaktywny
hive
Tryb Wsadowy:
hive -e ‘select foo from bar’
hive -f ‘/path/to/my/script.q’
hive -f ‘hdfs://namenode:port/path/to/my/
script.q’
więcej opcji: hive --help
71

Typy danych
INT, TINYINT, SMALLINT, BIGINT
BOOLEAN
DECIMAL
FLOAT, DOUBLE
STRING
BINARY
TIMESTAMP
ARRAY, MAP, STRUCT, UNION
DATE
CHAR
VARCHAR
72

Składnia zapytań
SELECT, INSERT, UPDATE
GROUP BY
UNION
LEFT, RIGHT, FULL INNER, FULL OUTER JOIN
OVER, RANK
(NOT) IN, HAVING
(NOT) EXISTS
73

Data Deﬁnition Language
• CREATE DATABASE/SCHEMA, TABLE, VIEW, FUNCTION, INDEX
• DROP DATABASE/SCHEMA, TABLE, VIEW, INDEX
• TRUNCATE TABLE
• ALTER DATABASE/SCHEMA, TABLE, VIEW
• MSCK REPAIR TABLE (or ALTER TABLE RECOVER PARTITIONS)
• SHOW DATABASES/SCHEMAS, TABLES, TBLPROPERTIES,
PARTITIONS, FUNCTIONS, INDEX[ES], COLUMNS, CREATE TABLE
• DESCRIBE DATABASE/SCHEMA, table_name, view_name
74

Tabele
CREATE TABLE page_view(viewTime INT, userid BIGINT,
page_url STRING, referrer_url STRING,
ip STRING COMMENT 'IP Address of the User')
COMMENT 'This is the page view table'
PARTITIONED BY(dt STRING, country STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '001'
STORED AS TEXTFILE;
75

Pierwsze kroki w Hive
CREATE TABLE tablename1 (foo INT, bar STRING) PARTITIONED BY (ds STRING);
LOAD DATA [LOCAL] INPATH 'ﬁlepath' [OVERWRITE] INTO TABLE tablename1;
INSERT INTO TABLE tablename1 PARTITION (ds='2014') select_statement1 FROM
from_statement;
76

Pierwsze kroki w Hive
UPDATE tablename SET column = value [, column = value ...] [WHERE expression]
DELETE FROM tablename [WHERE expression]
77

Inne formaty plików? SerDe
127.0.0.1 - frank [10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif HTTP/1.0" 200 2326 "http://www.example.com/
start.html" "Mozilla/4.08 [en] (Win98; I ;Nav)"
CREATE TABLE apachelog (
host STRING, identity STRING, user STRING, time STRING, request STRING, status STRING,
size STRING, referer STRING, agent STRING)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
"input.regex" = "([^]*) ([^]*) ([^]*) (-|[^]*]) ([^ "]*|"[^"]*") (-|[0-9]*) (-|[0-9]*)(?:
([^ "]*|".*") ([^ "]*|".*"))?"
)
STORED AS TEXTFILE;
78

Inne formaty plików? SerDe
CREATE TABLE table (
foo STRING, bar STRING)
STORED AS TEXTFILE; ← lub SEQUENCEFILE, ORC, AVRO lub PARQUET
79

Zalety, wady, porównanie
Hive Pig
deklaratywny proceduralny
tabele tymczasowe pipeline
polegamy na optymalizatorze
bardziej ingerujemy w
implementacje
UDF, Transform UDF, streaming
sterowniki sql data pipeline splits
80

Stinger
http://hortonworks.com/labs/stinger/
81

Tips & Tricks
hive.vectorized.execution.enabled=true
ORC
hive.execution.engine=tez
John Lund Stone Getty Images
82

Co dalej?
• Integracje z Solr, Elastic, MongoDB
• UDF
• multi table inserts
• JDBC
83

Warsztat
źródło:HikingArtist84

Monitorowanie i
zarządzanie klastrem na
przykładzie Ambari
85

CLI
• Yarn Administration Commands
• resourcemanager nodemanager proxyserver rmadmin daemonlog
• HDFS
• User Commands
• dfs
• fsck
• Administration Commands
• datanode dfsadmin namenode
• HBase
• start-hbase.sh, stop-hbase.sh
• HCatalog, HiveServer,
• Kafka, Storm, Tez, Spark, Oozie i inne
• monitoring, konfiguracja, aktualizacja
86

Ambari
• zarządzanie klastrem
• konsola monitoringu
• instalacja nowych węzłów
• konﬁguracja
• wygaszanie serwerów
87

Chcesz wiedzieć więcej?
Szkolenia pozwalają na indywidualną pracę z każdym
uczestnikiem
• pracujemy w grupach 4-8 osobowych
• program może być dostosowany do oczekiwań
grupy
• rozwiązujemy i odpowiadamy na indywidualne
pytania uczestników
• mamy dużo więcej czasu :)

Szkolenie dedykowane dla
Ciebie
Jesteś architektem lub team leaderem?
• na przekrojowym szkoleniu 5-dniowym omawiamy i
ćwiczymy cały ekosystem Hadoopa
• na szkoleniu dedykowanym dla architektów dyskutujemy
o projektowaniu systemów BigData
Jesteś analitykiem?
• na dedykowanym szkoleniu przećwiczysz w szczegółach
Pig i Hive i rozwiążesz przykładowe problemy analityczne

Szkolenie dedykowane dla
Ciebie
Jesteś programistą?
• szkolenie 3-dniowe pozwala w szczegółach zapoznać się z
programowaniem zaawansowanych aspektów MapReduce
w Javie i programowaniem w podejściu strumieniowym
Interesuje Cię całość zagadnienia BigData?
• Przetwarzanie Big Data z użyciem Apache Spark
• Bazy danych NoSQL - Cassandra
• Bazy danych NoSQL - MongoDB

źródła
• HikingArtist.com - rysunki
• hortonworks.com - architektura HDP
• apache.org - graﬁki Pig, Hive, Hadoop

Wprowadzenie do
technologii Big Data
Radosław Stankiewicz - radoslaw@zagwozdka.com
www.sages.com.pl

Wprowadzenie do technologii Big Data / Intro to Big Data Ecosystem

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Wprowadzenie do technologii Big Data / Intro to Big Data Ecosystem