For WizardKim-DPT1


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Big Data Analytics: Analysis of Features and Performance of Big Data Ingestion Tools

Andreea MĂTĂCUȚĂ, Cătălina POPA

The Bucharest University of Economic Studies, Romania

[email protected], [email protected]

The purpose of this study was to analyze the features and performance of some of the most

widely used big data ingestion tools. The analysis is made for three data ingestion tools,

developed by Apache: Flume, Kafka and NiFi. The study is based on the information about tool

functionalities and performance. This information was collected from different sources such as

articles, books and forums, provided by people who really used these tools. The goal of this

study is to compare the big data ingestion tools, in order to recommend that tool which satisfies

best the specific needs. Based on the selected indicators, the results of the study reveal that all

tools consistently assure good results in big data ingestion, but NiFi is the best option from the

point of view of functionalities and Kafka, considering the performance.

Keywords: Big Data, Data ingestion, Real-time processing, Performance Functionality, Data

Ingestion Tools


During the last years, the technology had a

big impact on the applications and in the

processing of data, and organizations have

begun give more importance to data and

invest more in their collection and

management. Big Data created as well a new

era and new technologies that allow analysis

types of data like text and voice, which have a

huge volume in the Internet and in other

structures digital. The evolution of data is

spectacular and it is very important to mention

in this paper that in the past, the volume of

data was at the level of bytes and nowadays

the companies use a huge volume of data at

the level of petabytes. Experts from the

National Climatic Data Center in Asheville

estimated that if we want to store all the data

that exist in world we had need at least 1200

exabytes, but is impossible to pin down a

relevant number. Maybe these sizes do not

mean something for the people who do not

have a direct contact to big data, but the

volume of data is huge and it is very difficult

to understand what these numbers mean.

V. Mayer-Schönberger and K. Cukier

mentioned in [24] that “There is no good way

to think about what this size of data means” to

prove once that big data is in a continuous

evolution and the future of it will be

gloriously. The paper presents an analysis of

the use of big data ingestion and present a

research used to evidence the functionality

and performance of most widely tools. We

first introduce some concepts about data

ingestion and the importance to choose it to

process big data and we propose to do a short

description for the tools used in analyze,

offering some information about Hadoop


We then review existing three Apache

ingestion tools: NiFi, Flume and Kafka in

processing of big data and we will examine

the differences between them and the strong

parts of each of them. We want to offer

systematic information of the main

functionality of three tools developed by

Apache: Flume, Kafka and NiFi used in data

ingestion process and a detailed way how to

combine the tools to improve the results for

your requirements using for our research

different ways to compare the tools based on

performance, functionalities, the complexity.

Our analysis shows that all three tools have

something special, but there is not a one and

only tool which address all of customer’s

requirements and the combination of tools is

the answer for that problem. We examine and

recommend all the possible combinations

based on the needs of customers.

The paper analyses the main characteristics of

data ingestion tools. It provides key

information about typical issues of data

ingestion and about the reasons why we


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choose the three Apache ingestion tools

instead others. Using a preliminary view, it is

important to identify the common

characteristics for tools. After that, the

analysis results will be providing. We decided

to examine Apache tools because Apache is

very well known in developer’s area and it is

the most used web server software, running on

67% of web servers from entire world.

According to the [3], “The name 'Apache' was

chosen from respect for the Native American

Indian tribe of Apache, well-known for their

superior skills in warfare strategy and their

inexhaustible endurance.”

The paper has the following structure. Section

2 introduced the concept of data ingestion

with big data, the necessity of it, including a

short description for Hadoop ecosystem and a

short paragraph where we offer information

about each tool. Section 3 contends our

research based on tools, analyzing the main

characteristics for NiFi, Flume and Kafka,

offers solid arguments why this paper use

them instead another developed tool and an

analyze based on the functionalities and

performance for them. Section 4 presents the

results of our research based on functionalities

and performance for the tools and a detailed

explanation for each result. Section 5, the

conclusion section is the most important part

because here we can observe the real

importance of the information found in this

paper and the scope of it and contains our final


2. Data ingestion and Hadoop ecosystem

2.1 Data ingestion concept According to [9], “in typical ingestion

scenarios, you have multiple data sources to

process. As the number of data sources

increases, the processing starts to become

complicated”. For a long time, data storage

does not need additional tools to process the

volume of data because the quantity was

insignificant, but in last years when the

concept of big data had appeared that begin to

be a problem. As we mentioned in

introduction, this paper analyses a new

process to obtain and import data for their

storage in a database or for immediate use

called “data ingestion”. According to [27], the

term “ingestion” means a consumption of a

substance by an organism, in our paper the

consumption of a substance is represented by

data and the organism can be, for example a

database where the data are storage.

Data ingestion layer represents the initial step

for the data coming from different sources, the

step where they are categorized and

prioritized, but it is important to note that is

close the toughest job in the process of big

data. N. S. Gill [26] mentioned that “Big Data

Ingestion involves connecting to various data

sources, extracting the data, and detecting the

changed data”. For unfamiliar readers, data

ingestion can be explained like moving data

(structured or unstructured) from their origin

into a system where is easy to be analyzed and

stored. In the next paragraphs of this paper we

find important information about data

ingestion from different perspectives: we

prove the necessity of data ingestion, we note

the challenges met in data ingestion, we offer

information about parameters and key


To finish the process of data ingestion it is

necessary to use a tool that is capable to

support the following key principles: network

bandwidth, unreliable network, choosing

right data format and streaming data

2.1.1 The necessity of data ingestion

In many situations, when using big data, the

source of data structure is not known and if the

companies, for example use the common data

ingestion methods it is difficult to manipulate

the data. For the companies data ingestion

represents an important strategy, helping them

to retain customers and obtain increase


The main advantages that demonstrate the

necessity of data ingestion are the following:

 Increased productivity. It is taking a lot of time for companies to analyze and to

move data from different sources, but with

data ingestion the process is easier and the

time can be used to do something else

 Ingestion of data in batches or in real time. In batches, data are stored based on

periodic intervals of time

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 Data are automatically organized and structured, even if there are different big

data formats or protocols.

2.1.2 Data ingestion challenges

Variance and volume of data sources are in a

continuously expansion. Extracting data from

these sources can be extremely challenging

for users, considering the required time and

resources. The main issues in data ingestion

are the following:

 Different formats in data sources

 Applications and data sources are evolving rapidly

 Data capture and detection are time consuming

 Validation of ingested data

 Data compression and transformation before ingestion

2.1.3 Data ingestion parameters

The main ingestion parameters used in the

comparison are the following:

 Data velocity- this parameter is based on the speed to process data from different

sources like human interaction, social

media, networks.

 Data size- Because data ingestion works with huge volume of data, they are

generated from multiple sources to

increase the time

 Data Frequency-This parameter can have two ways to process data: in real time or


 Data Format-Every company choose different format for their data and the data

ingestion needs to adapt for every


2.2 Hadoop ecosystem

Apache Hadoop ecosystem is an essential

supporting structure for processing and

storing large amount of data (Big Data). The

Apache Hadoop ecosystem grows

continuously and it consists of multiple

projects and tools with valuable features and

benefits that provide capacity of loading,

transferring, streaming, indexing, messaging,

querying and many others. Hadoop contains

two main elements: Hadoop Distributed

Filesystem (HDFS) and MapReduce. The

HDFS is a file system designed for data

storage and processing of data. HDFS is made

for storing and providing streaming, parallel

access to large amount of data (up to 100s of

TB). HDFS storage is distributed over a

cluster of nodes. MapReduce is a large dataset

processing model. As the name suggests, it is

composed of two steps. The initial step, Map,

establishes a process for each single key of the

records to be processed (key value type). The

final step, Reduce, performs the operation of

summing the results, processing the data

output from the map phase according to the

required operator or function, resulting in a set

of value key pairs for each single key.

Swizec Teller notes in [22] that these two

projects can be configured in combination

with other projects into a Hadoop cluster. A

cluster can have hundreds or thousands of

nodes and they can be difficult to manually

configure. Hadoop cluster covers the need for

tools to easily and effectively configure

systems and data. HDFS and MapReduce

might be executed on separate servers. They

are named Hadoop clients and security is the

main reason for physically separating Hadoop

nodes from Hadoop clients. If we are deciding

to install clients on the same servers as

Hadoop, we will have to provide a high level

of security to every user for access. Logically

and physically separating them simplifies the

needed configuration steps. There are many

sub-projects (managed mostly by Apache,

which are made by free organizations)

designed for maintenance and monitoring that

very well integrate with Hadoop and they lets

us concentrate for developing data ingestion

rather than monitoring it. Three of the

commonly used tools for data ingestion in

Hadoop are rigorously described in the

following sections.

2.3 Kafka

Apache Kafka is a distributed, high-

throughput messaging system, a publish -

subscribe environment that provide highly

availability. With one broker handling

hundreds of MB per second of reads and

writes from several clients, Kafka is a very

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fast system. Replication is used for messages

across the cluster and then stored on disk.

According to [7], “Kafka can be used for

stream processing, web site activity tracking,

metrics collection and monitoring, and log

aggregation”. “It is a paradox that a key

feature of Kafka is its small number of

features. It has far fewer features and is much

less configurable than Flume”, noted Ellen

Friedman and Ted Dunning in [8]. Flume will

be described in the next section of this paper.

They also discovered that “Kafka is similar to

Flume in that it streams messages, but Kafka

is designed for a different purpose.

While Flume is designed to stream messages

to a sink such as HDFS or HBase, Kafka is

designed for messages to be consumed by

several applications”. The principal elements

of Kafka architecture are Producer, Broker,

Consumer and Topic. Topics are used for

feeding of messages. Producers send the

messages to topics and consumers, who can

subscribe to those topics and consume the

messages from that topics. Topics are

partitioned and is attached a key to each

message and a partition is like a log.

2.4 Flume

“Flume is a distributed, reliable, and available

service for efficiently collecting, aggregating,

and moving large amounts of log data.” is a

reliable definition found on official website

for Apache Flume. This paper analyses the

newest version for tools and the last version

stable for Flume is 1.8.0, the eleventh release

for apache project that offer to the users a

stable product, compatible with older versions

of the Flume (1.x code line) and it is a

software ready for production. This tool is

made to ingest and collect huge volumes of

data from multiple sources into Hadoop

Distributed File System (HDFS), the most

used types of data for processing are sensor

and machine data, social media data, maps,

astronomy, aerospace, application logs or

geo-location data. We observe the using of

Flume in one specific example where the tool

is used for the logging of manufacturing

operations, the log is generated in every run of

the product when it comes off the line and

generate a file with information about the

respective run. In a day the product runs for

thousands of times and generate a large

volume of data stored in log files and using

Flume, data can be stream into a tool for

analyze, like we can see in the image below,

followed by storage process of them in HDFS.

We remark that in general, Flume allows users

to ingest and store into Hadoop for future

analyses, data from multiple sources and with

different sizes, use horizontally scale to ingest

data, the user has the guarantee that his data

are delivered based on the transactions

between agents, use the insulate system in the

situation when incoming data rate is bigger

than a standard rate and it has a better

integrated bond with Hadoop ecosystem in

contrast to Kafka or NiFi.

J. Kim and B. Bengfort note in [11] that the

data flows in Flume like a pathway which

ingest data from origin to destination. Data or

events are moved from source to destination

based one sequence of hops and the concept is

named Flume agent (a JVM process) which

consist three important components: channel,

sink and the source as we can see in the below


Source represents the part of the Agent where

data are received from data generators,

followed by transfer them to the channels

from Flume events, Channel can work with

different sources and sinks and are

represented a bridge between the sinks and the

sources: receives the data from the source and

use the buffer till they are consumed by sinks.

Sink represents the final component of the

Flume agent where the data from the channels

are consumed and send to the destination (the

data are stored in HDFS at this step). We note

that the biggest disadvantage of using Flume

is that the data can be lose in a very easy way,

for example if the user choose the Memory

channel with high throughput, when the agent

node goes down the data will be lost.

2.5 NiFi

Some systems are generating the data and

other systems are consuming it. Apache NiFi

is developed for the automation of this flow.

In [16] Apache NiFi is defined as “a data flow

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management system that comes with a web

User Interface that helps to build data flows in

real time, it supports flow-based programming

and the graph programming includes

processors and connectors, instead of nodes

and edges”. The user connects processors

together with connectors and the data will be

defined how to be manipulated. A strong

feature is Nifi’s capability of ingesting any

data using ingestion methodologies for any

particular data. Similar to inputting data. The

output is very customizable too. T. John and

P. Misra observed in [23] that “The Apache

NiFi website states Apache NiFi as - An easy

to use, powerful, and reliable system to

process and distribute data”. We consider that

is a good alternative of Apache Flume having

a vast set of features and easy to use web user

interface. It is easy to set-up and it is very

highly customizable.

3. The research methods This section contains information about the

analysis of the main characteristic for tools:

Flume, NiFi and Kafka and represents our

analysis of the functionalities and

characteristics with the scope to put in

evidence the choice of them for creating the

content of this paper. Our analysis is based on

the comparison of them from different

perspectives like performance, functionality,

necessity. First, when we searched

information about data ingestion tools we

found over 30 different tools used by

companies in this process and we were in the

situation to choose the best of them for our

analysis. The choice of the data ingestion tool

for a company depends on multiple factors

such as target, transformations (simple or

complex), data source, performance, necessity

so we used the same criteria in our research.

Next, we searched for articles and opinions

that contained the key words like “data

ingestion used tool”, “first option of data

ingestion tool” and we obtained the main used

tools for data ingestion. The final decision was

based on [18] were, based on top 18 data

ingestion tools, Flume is on second position,

followed by Apache Kafka and Apache NiFi,

first option been Amazon Kinesis. Based on

this top we decided that our paper will analyze

three tools which represent a main choice for

companies and users. We preferred to use in

our research Apache tools mentioned in this

paper because an advantage of them is the

possibility to combine them for a better result.

Comparing with the other tools from data

ingestion area, we noted that the analyzed

tools from this paper have some special

characteristics such as the guarantee that they

are reliable offering zero data loss, using large

volumes of data Apache Kafka and Flume

systems provide scalable, reliable and high-

performance. In our decision we based on

criteria which convinced us that if we are put

in a hypothetical situation to choose a tool for

data ingestion we will use one of them.

The last criteria and maybe the most important

which helped us to decide on this choice was

the information from articles and books such

as [16], [17] and [21], based on using of this

tools in known application. We find that the

main criteria when a company wants to

choose a tool for data ingestion are: speed to

ingest data in a rapid way, platform support

which offers the facility to connect with data

stores, the facility to scale the framework to

work with large datasets and the facility to

extract and access data from sources without

impact on their ability to execute transactions

or performance and in our choice for NiFi,

Kafka and Flume we used that criteria.

3.1 Functionalities of analyzed tools

An important part of the study is the analysis

of tools functionalities. In this subsection we

present the functionalities used for tool

comparison. We analyzed the following

indicators: reliability, system requirements,

limits of the tool, stream ingest and

processing, guaranteed delivery and data type.

In the following paragraph we will justify our

choice for these indicators. In the next

paragraph, we will present the results. In the

result section we examine the functionality for

each tool using a standard scale, with three

values: complete implemented functionality,

partial implemented functionality and no

implemented functionality.

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According to [25], “data collection and

transportation should be reliable with

minimum data loss”. In our research,

reliability was based on the ability to deliver

events and logging them in situation when one

of the tools presented have a software crash,

scarce memory or bandwidth and the

possibility to lose data can appear. We

consider that guaranteed delivery

functionality is very important because user

needs to have the guarantee that his data are

completed after data ingestion process. Limits

of the tools were included in our analysis to

expose their disadvantages.

We want to note in this paper the fact that we

consider that from this point of view a perfect

tool does not exist and for companies can be a

better solution to combine them. Another

important functionality used in our

investigation was data type because it is

important for the user to know what type of

data can ingest with the chosen tool.

3.2 Performance measurements of the

selected tools

In performance testing using big data are

included two main actions: data ingestion and

throughout where is verified how the fast

system can consume data from various data

source and data processing which involves to

verify the speed with which the map or queries

can reduce jobs in execution. We note in our

research performance measurements because

we consider that the user needs to know

information about speed, the number of

processed files per minute, the acceptable size

for the files for the tool. In our analyze we

based on performance measurements studies

created on the tools official pages and on the

opinions found in articles, from different users

who use Kafka, NiFi or Flume. In this paper,

we used for our research in performance the

following indicators: speed, number of

processed files per second, scalability and

message durability.

For businesses can be challenging to ingest

big data at a reasonable speed or to process it

efficiently with the scope to maintain a

competitive advantage so the speed indicator

needs to be included in our analysis. Another

indicator included in our research was the

number of processed files per second. We

want to note the fact that for our tools the

number of processed data per second differed

because of the size of files. Scalability was

included in our research based on what Cory

Isaacson said in [5]: “When managing a

successful expanding application, the ability

to scale becomes a critical need. Whether you

are introducing the latest new game, a highly

popular mobile application, or an online

analytics service, it is important to be able to

accommodate rapid growth in traffic and data

volume to keep your users happy.”. We

consider that indicator an important one

because data ingestion tools work with a huge

volume of big data and the scalability can help

in this process. Message durability was

included in our research to make sure that

even if the tool dies, the task is not lost. When

one of tools quit or crash the information

about queues and messages are forgot and to

make sure that messages are not lost it is

necessary to mark both the and messages

queue as durable. In the result section we

examine the performance indicators for each

tool using a standard scale which can have

three values: high, medium and slow.

Bellow, we present a performance

measurements study for Flume, presented on

the Flume’s official page and we used it in our

research because it is the most explicit study

found on this subject which evidence

performance for Flume in big data ingestion

process. Test was made by M. Percy in [15]

who used the following test setup: Flume

agent was run in a single JVM on his own

physical machine and a separate client

machine was used to generate load in syslog

format against the Flume box. Data was store

by Flume onto a 9 node HDFS cluster which

was configured on a separate hardware. In this

test, virtual machines were not used. On the

point of view of hardware specifications CPU

used was Intel Xeon L5630 2 x quad-core with

Hyper-Threading @ 2133MHz (8 physical

cores), memory: 48GB and operation system

SuSE Linux 64-bit. For Flume configurations,

Mike Percy used 1.6.0u26 java version, one

agent, for channel used Memory Channel and

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for sink HDFSEventSink with Avro event

serialization and snappy serialized

compression. For data description he used

event size 300 bytes. According to the

obtained results, Flume is capable to assure an

approximate average of performance equal

with 70000 events/second, on a single one

machine, without data loss during the test.

4. The research results 4.1 The functionality comparison results

Based on the results obtained in our research,

we compare the tools from the functionality

point of view for a better analyze (see Table

1). According to definition from Wikipedia

for Apache Flume, “is a distributed, reliable,

and available service for efficiently collecting,

aggregating, and moving large amounts of log

data” in our research we can prove that this

functionality is completed implemented and

Flume have a fail over mechanism that can

move the data flows on a new agent without

exterior interventions. We consider that the

best choice for this functionality is Kafka

because in situation when a single point

failure data is available in contrast of Flume

where the user cannot access events till the

disk is recovered. On the other hand, Nifi is

reliable throw definition, but in real world is

better to combine this tool with Kafka for

using Kafka’s reliable data stream storage.

Flume and Nifi represent the main choices for

data guarantee delivery in comparison with

Kafka where that is not guarantee in totality.

Depending on protocol, NiFI allows supports

guaranteed delivery with the mention that

supports most once or at least once and Flume

guarantee the delivery of the Events using a

transactional approach. All of them have a

limitation for functionality “limits of the

tools” because a one and only tool does not

exist to be addressed for all requirements or to

do everything. In our analyze we obtained a

list of limits for every tool. For Flume we

obtained that when Kafka Channel is used the

possibility to loss data appear, data are limited

at kb dimension and the data replication does

not exist. On the other hand, Kafka has the

same problem as Flume for dimension of data

(kb), it has fixed protocol, format and schema,

a custom code is often needed. The last one,

NiFi does not accept data replication and it is

not a variant to use for CEP or windowed

computations. Data type for Flume is

represented by the next file formats Sequence

File, DataStream or Compressed Stream,

Kafka accept data type like JSon, PoJo or Java

bean and the fastest way: arrays, Nifi uses data

object (Flow File). Conclusion for this

functionality is that a tool that can process all

types of data does not exist and the decision

for the user depends on his needs. We consider

that Nifi is the best option from the point of

view of system requirements because can run

on laptop and can be used with a cluster across

enterprise class servers, hardware and

memory needed depends on the size of data,

can run on Windows, Linux, Unix or Mac OS,

support all type of browser and requires java

8 or newer. On the other hand, Flume can run

only on Linux, requires java 8 or newer,

requires sufficient space on disk for sinks and

channels and the agents need permission to

Write/Read directories. Kafka requires

machines with a lot of memory on them, can

run on Unix or Windows, requires java 8 or

newer. Using Hadoop, Flume can be used to

transfer, collect, aggregate streaming events

because it is a distributed system, while

simple, flexible and intuitive programming

model is based on streaming data flows. Our

analysis provides the fact that Flume

maintains a main list of ongoing data flows. In

Kafka, messages are put into topics, which are

split into partitions and this one is replicated

across the nodes in the cluster. Kafka provide

a huge throughput persistent messaging which

is used to scalable and allow parallel data

loads for Hadoop. NiFi provides real-time

control and it is easier to manage when run in

a cluster the movement of data between

source and destination. In conclusion from the

point of view of functionality, according to

our analyze we consider that NiFi represents

the best solution to use in a company.

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Table 1. Functionality tools comparison

Functionality Flume NiFi Kafka Recommended tool

Reliability Partial





















Flume and NiFi

Data type Partial









Flume, NiFi and














Stream ingest

and processing










Flume, NiFi and


Limits of the tool Partial









Flume, NiFi and


4.2 The performance comparison results

In the table 2, we did a summary for

performance indicators for Kafka, Flume and

NiFi based on the results of our analysis. To

determine the measure for tools in our

research we based on the functionality results

where we note that Flume and Kafka have a

limit for data size (KB), so from this point of

view Flume and Kafka are the best options for

the number of files processed per second

because the size of them is small. Speed of

tools was put to medium for all of them

because the indicator does not have a standard

limit, it depends on the needs of the user and

if the user wants to increase it can use tuning

procedure. According to [14], “Kafka is a

general purpose publish-subscribe model

messaging system, which offers strong

durability, scalability and fault-tolerance

support.” we consider that this tool is the best

option for scalability in comparison with NiFi

and Flume which are distributed, reliable, and

available systems. Kafka is very scalable and

one of the key benefits of it is that adding a

large number of consumers can be made in an

easy way without down time or affecting

performance in comparison with Flume or

NiFi where this process cannot be made in an

easy way. For the last one indicator we

obtained that Flume represents one of the best

choice because it supports multiple

interceptors chaining and data flow models

and with them, flume makes event

transforming and filtering very easy and

Kafka supports replication synchronous and

asynchronous based on the durability

requirement and uses commodity hard drive.

On the other hand, NiFi do not have support

native for message processing and in this case

the tools need to integrate with other event

processing frameworks to complete the job. In

conclusion our choice from the point of view

of performance indicators is Kafka because it

obtained good results for processing, message

durability and scalability.

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Table 2. Performance indicators

Performance indicator Flume NiFi Kafka The best choice

Speed Medium Medium Medium All of them

Number of files processed per second High Medium High Flume and Kafka

Scalability Medium Medium High Kafka

Message durability High Low High Flume and Kafka

5. Conclusions

The complexity and volume of data generated

by human and machines activity is increasing

continuously. This paper presented an

analysis of the use of big data ingestion and

the process of ingesting the variety, volume

and veracity of Big Data.

After introducing the concept of data

ingestion with Big Data, the necessity of it,

and realizing a short description for Hadoop

ecosystem and a description of three of most

widely tools for big data ingestion, we

examined these three Apache tools NiFi,

Flume and Kafka in order to determine the

common characteristics and analyze the parts

of each other according performance and

functionality. This research showed that in

terms of performance, Kafka offers the best

results, and in terms of functionality, Nifi is

the best option.


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Andreea MĂTĂCUȚĂ has graduated the Faculty of Cybernetics, Statistics

and Economic Informatics from the Bucharest University of Economic

Studies in 2016. She graduated the master at the same faculty, in Economic

Informatics and this year (2018) she got her master diploma. Currently she

has a full job as programmer using Java at the Axway Company and in the

past she worked on C#. Her main focus is to learn more about programming

and to become better on his domain. About her personal life she is passionate about technology,

reading, pets, sport.

Catalina POPA has graduated the Faculty of Electronics,

Telecommunications and Information Technology from Polytechnic

University of Bucharest in 2016. She graduated in June 2018 the master of

Economic Informatics at Faculty of Economic Cybernetics, Statistics and

Informatics from Academy of Economic Studies. Currently she is a Business

Intelligence Application Administrator at Orange, in IT Services Operations

Department. Her work focuses on improving the monitoring systems by

adding and modifying Shell and PL/SQL scripts

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