Automized Data Preparation: The most important things to consider when building a data pipeline

Introduction to the series

The abili­ty to deri­ve insights and value from data is a key fac­tor for the future via­bi­li­ty of com­pa­nies. In this con­text, the company’s data stra­tegy as well as the tech­ni­cal and data skills of employees play a major role.

A pre­re­qui­si­te for buil­ding ana­ly­ti­cal solu­ti­ons from models and algo­rith­ms is that the data is available relia­bly and fit­ting to the various needs of stake­hol­ders in the com­pa­ny on the one hand, and that it is available in a form and struc­tu­re that enables fur­ther pro­ces­sing on the other.

To meet the­se con­di­ti­ons, it is important to build an auto­ma­ted data pipe­line:
The pipe­line is a struc­tu­red sequence of pro­ces­ses desi­gned to effi­ci­ent­ly and relia­bly rou­te data from cap­tu­re to pre­pa­ra­ti­on to ana­ly­sis. When you want to do that, you encoun­ter seve­ral chal­lenges.

In a new series, we will address the­se chal­lenges and show which aspects are rele­vant or how solu­ti­ons can be built. This blog post will first give an over­view of the topic befo­re we will go into more depth on indi­vi­du­al aspects in upco­ming posts.

// 1 Data Extraction / Data Ingestion / Data Acquisition

A first step in deve­lo­ping a robust and effi­ci­ent data pipe­line is the data inges­ti­on pro­cess. This pro­cess invol­ves coll­ec­ting and import­ing data from various sources. During this pha­se, chal­lenges can ari­se that can hin­der the deve­lo­p­ment of a smooth data pipe­line.

// Hete­ro­gen­ei­ty of data sources and data struc­tures
Data can come from a varie­ty of sources, inclu­ding data­ba­ses, files, APIs, and strea­ming plat­forms. Each source can have its own data for­mat, struc­tu­re, and flow. The­r­e­fo­re, it is important to imple­ment mecha­nisms to over­co­me this hete­ro­gen­ei­ty, such as trans­forming the data into a uni­form for­mat or imple­men­ting adap­ters for spe­ci­fic data sources.

// Ensu­ring Data Qua­li­ty and Inte­gri­ty
In the con­text of data inges­ti­on, the­re is a risk of erro­n­eous or incom­ple­te data ente­ring the pipe­line. It is the­r­e­fo­re neces­sa­ry to imple­ment data vali­da­ti­on and cle­an­sing mecha­nisms to ensu­re that only high qua­li­ty data enters the pipe­line. This can be achie­ved by using data vali­da­ti­on rules, che­cking data for con­sis­ten­cy, and imple­men­ting error hand­ling mecha­nisms.

// Thin­king about poten­ti­al requi­re­ments: sca­la­bi­li­ty
With big data, sca­la­bi­li­ty of the data inges­ti­on pro­cess can beco­me a chall­enge. Pro­ces­sing lar­ge amounts of data effi­ci­ent­ly requi­res robust and powerful sys­tems. One way to address this issue is to use sca­lable data pro­ces­sing frame­works such as Apa­che Hadoop or Apa­che Spark to pro­cess the data in par­al­lel and dis­tri­bu­te the workload across mul­ti­ple nodes. A par­ti­cu­lar chall­enge in this con­text is the inges­ti­on of real-time data. For strea­ming, spe­cial frame­works such as Apa­che Kaf­ka are available to help pro­cess real-time data effi­ci­ent­ly and mini­mi­ze laten­cy.

// 2 Data Wrangling and Data Preparation

Data wrang­ling refers to the pro­cess of clea­ning, trans­forming, and inte­gra­ting raw, unstruc­tu­red, or unfor­mat­ted data to prepa­re it for fur­ther ana­ly­sis or use in a data pipe­line.

It invol­ves pre­pa­ring the data to ensu­re that it has a uni­form and con­sis­tent struc­tu­re, miss­ing values are addres­sed, inac­cu­ra­ci­es are cor­rec­ted, and poten­ti­al out­liers are iden­ti­fied and addres­sed. The focus is on data qua­li­ty and pre­pa­ring the data for fur­ther steps such as data ana­ly­sis or machi­ne lear­ning. The pur­po­se of Data Pre­pa­ra­ti­on is to prepa­re the data so that it is opti­mal­ly sui­ted for the spe­ci­fic use case or ana­ly­sis.

// Data trans­for­ma­ti­ons and for­mat­ting
In this step, the data is trans­for­med into a form sui­ta­ble for ana­ly­sis. This may include trans­forming data into nume­ric or cate­go­ri­cal values, sca­ling values, or crea­ting new cha­rac­te­ristics. The goal is to prepa­re the data for the desi­red ana­ly­ti­cal methods. For exam­p­le, cer­tain algo­rith­ms do not accept cate­go­ri­cal varia­bles. Some­ti­mes it may also be useful to stan­dar­di­ze or nor­ma­li­ze data to best prepa­re it for ana­ly­sis.

In addi­ti­on, it may be neces­sa­ry to con­vert data to a spe­ci­fic file for­mat or make adjus­t­ments to the column or row struc­tu­re or chan­ge the data orga­niza­ti­on.

// Data aggre­ga­ti­ons
To enable a hig­her level of ana­ly­sis, data is aggre­ga­ted or redu­ced. This can be done by grou­ping data by spe­ci­fic cha­rac­te­ristics, sum­ma­ri­zing data in spe­ci­fic time peri­ods, or by deri­ving key figu­res. Aggre­ga­ti­on faci­li­ta­tes the ana­ly­sis of lar­ge amounts of data and allows pat­terns or trends to be iden­ti­fied at a hig­her level.

// Data vali­da­ti­on and veri­fi­ca­ti­on
Pre­pared data must be che­cked for qua­li­ty, con­sis­ten­cy, and accu­ra­cy to ensu­re that it meets ana­ly­ti­cal requi­re­ments. This includes run­ning tests, veri­fy­ing data rela­ti­onships, and com­pa­ring results to expec­ted out­co­mes.


// 3 Data provision for business users

Each stake­hol­der has dif­fe­rent data requi­re­ments. It is important to under­stand the needs and expec­ta­ti­ons of each stake­hol­der and ensu­re that the data pro­vi­ded is rele­vant to their spe­ci­fic tasks and ana­ly­ses.

// Data Access and Secu­ri­ty
Dif­fe­rent stake­hol­ders have dif­fe­rent requi­re­ments for acces­sing the data. It is important to ensu­re that data access is gover­ned accor­ding to poli­ci­es and per­mis­si­ons to ensu­re data con­fi­den­tia­li­ty and secu­ri­ty. Depen­ding on the sen­si­ti­vi­ty of the data, dif­fe­rent secu­ri­ty mea­su­res such as access con­trols, encryp­ti­on, or anony­miza­ti­on may be requi­red.

// Data under­stan­ding and docu­men­ta­ti­on
For stake­hol­ders to effec­tively use the data pro­vi­ded, it is important to pro­vi­de them with clear docu­men­ta­ti­on and meta­da­ta about the data struc­tu­re, the mea­ning of the fields, and any trans­for­ma­ti­ons.

// 4 No-code / low-code platforms as the means of choice

Soft­ware from Alte­ryx can signi­fi­cant­ly help build data pipe­lines effi­ci­ent­ly and redu­ce reli­ance on spe­cia­li­zed tech­ni­cal resour­ces.

Alte­ryx Desi­gner and Desi­gner Cloud pro­vi­de a user-fri­end­ly, gra­phi­cal inter­face through which data pro­ces­sing work­flows can be crea­ted by sim­ply drag­ging and drop­ping buil­ding blocks. This enables staff wit­hout exten­si­ve tech­ni­cal skills to per­form data pre­pa­ra­ti­on tasks.

Spe­ci­fic fea­tures and func­tion­a­li­ties can be acces­sed:

Work­flow auto­ma­ti­on: Once crea­ted, work­flows can be exe­cu­ted repea­ted­ly as new data beco­mes available, or they can be sche­du­led to pro­cess data updates auto­ma­ti­cal­ly.

Num­e­rous built-in con­nec­tions to various data sources, inclu­ding data­ba­ses, cloud sto­rage, APIs, and more. This faci­li­ta­tes access to mul­ti­ple data sources and redu­ces the time typi­cal­ly requi­red for the con­nec­tion estab­lish­ment and data pre­pa­ra­ti­on pro­cess.

Pre­de­fi­ned data trans­for­ma­ti­ons and ope­ra­ti­ons, such as fil­te­ring, mer­ging, aggre­ga­ting, cle­an­sing, con­ver­ting data types, etc. The­se pre­de­fi­ned func­tions great­ly sim­pli­fy data mani­pu­la­ti­on and redu­ce deve­lo­p­ment time.

Sca­la­bi­li­ty: Alte­ryx Desi­gner and Desi­gner Cloud are able to hand­le lar­ge amounts of data and deal with exten­si­ve data pro­ces­sing work­flows. This enables orga­niza­ti­ons to hand­le com­plex data pre­pa­ra­ti­on tasks and effi­ci­ent­ly sca­le their data ana­ly­sis pro­ces­ses.

Do you have ques­ti­ons about Data Pre­pa­ra­ti­on or Alte­ryx? Cont­act us!

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Sasha Shiran­gi (Head of Sales)