How modern technologies optimize data analysis resources
Functioning data pipelines are essential for data-driven companies. Data pipeline tools such as Fivetran or dbt reduce the complexity and maintenance effort required to build data pipelines reliably and independently without leaks. Data analysts in particular benefit from this.
Data analysis is a highly dynamic business. Data is extracted, transformed, combined, validated and loaded. Data pipelines not only ensure automated processes. They also keep the movement of data stringent and consistent. With data pipelines, companies ensure the professional preparation and processing of their data. Data ingestion, i.e. the connection of data, is an important basic building block within the modern data stack and requires a reliable structure.
The data pipeline as a production line
What motivates companies to use data pipelines? The following analogy describes the motivation very well. There are various production processes in industry, including so-called series production in the manufacturing sector. Different products and materials are combined in a production line. Initially, it was trained specialists who took care of production and processing. Henry Ford developed these production lines further and provided the workers with machines so that they could carry out their respective work steps more efficiently. The machines were arranged one behind the other in the order in which the work was carried out.
This not only reduced the workload for the employees. The respective work step could now also be carried out by those who did not have to be trained for the specific activity, but were above all experienced in handling the machines. In turn, the techniques and sequences could be continuously developed. An efficient and scalable business model.
Tool-based or do-it-yourself?
<a href="https://www.taod.de/services/data-engineering-consulting“ data-webtrackingID="blog_content_link" > Moderne Data Pipelines </a> sind nichts anderes als automatisierte und aufeinander aufbauende Prozesse innerhalb einer Fertigungsstraße. Sie sorgen für die Verarbeitung der Daten und speichern diese an einem zentralen, ausgelagerten Ort, beispielsweise einem Data Lake oder Data Warehouse. Auch wenn Echtzeit- oder hochentwickelte Datenanalysen benötigt werden oder die vollautomatisierte Speicherung von Daten in der Cloud gewünscht ist, sind Data Pipelines ein unersetzbares Werkzeug. Die meisten Unternehmen werden ohne sie nicht in der Lage sein, valide Datenanalyse zu betreiben. Deshalb stellt sich nicht mehr die Frage, ob Data Pipelines eingerichtet werden sollten, sondern auf welchem Weg dies mit welchen Ressourcen erledigt werden kann.
Früher wurden Daten aufwendig durch in Code entwickelte ETL-Pipelines bereitgestellt. Doch der interne Aufbau und die Pflege eigener Daten-Pipelines sind ein aufwendiges Vorgehen. Zunächst muss eine Methode zur Überwachung eingehender Daten entwickelt werden. Dann besteht die Notwendigkeit, zu jeder Quelle eine Verbindung herzustellen und Daten umzuwandeln, damit sie mit Format und Schema des Ziels übereinstimmen. Daten müssen in eine Zieldatenbank oder in ein <a href="https://www.taod.de/services/data-engineering-consulting“ data-webtrackingID="blog_content_link" > Data Warehouse </a> verschoben werden. Bei veränderten Unternehmensanforderungen wird das Hinzufügen und Löschen von Feldern und das Ändern ganzer Schemata notwendig. Zudem ist der Aufbau einer Datenbank-Modellierung inklusive Transformationen gefragt. Nicht zuletzt steht ein Data Team vor der fortlaufenden, permanenten Verpflichtung zur Pflege und Verbesserung der Daten-Pipeline und Schnittstellen.
Data pipeline tools relieve engineers and empower analysts
Diese Prozesse sind kostspielig, sowohl in Bezug auf Ressourcen als auch auf Zeit. Es wird erfahrenes und damit teures Personal aus dem Bereich <a href="https://www.taod.de/services/data-engineering-consulting“ data-webtrackingID="blog_content_link" > Analytics Engineering </a> benötigt, das entweder eingestellt oder geschult und von anderen Projekten und Programmen abgezogen werden muss. Der Aufbau kann Monate dauern, was zu erheblichen Opportunitätskosten führt. Nicht zuletzt skalieren diese Art von Lösungen nicht immer, sodass zusätzliche Hardware und Mitarbeitende benötigt werden, was schnell zulasten des Budgets geht. Der Bau eigener Data Pipelines macht meist nur in Ausnahmefällen und unter bestimmten Voraussetzungen Sinn.
Heute befähigen Technologien und Data Pipeline Tools auch Data Analysts dazu, nach kurzer Einarbeitungszeit eigenständig hochwertige Pipelines zu bauen, was vor allen Dingen für immer wiederkehrende Anforderungen eine hervorragende Lösung ist. Analytics Engineers werden zudem entlastet und verwenden ihre Ressourcen auf komplexeren Projektanforderungen. Der Umgang mit Data Pipeline Tools wie <a href="https://www.taod.de/tech-beratung/dbt-labs“ data-webtrackingID="blog_content_link" > dbt </a> oder Fivetran ist mit grundlegendem Know-how in den Bereichen <a href="https://www.taod.de/services/data-engineering-consulting“ data-webtrackingID="blog_content_link" >Datenanbindung </a> und Analytics schnell zu erlernen – ganz im Sinne von Henry Ford.
Three good reasons for Modern Data Pipelines
Number 1: Flexibility of the cloud
Business users generally need data on demand. However, time-consuming and sometimes even nerve-wracking requests to IT are the order of the day. These are often associated with the fear of receiving incomplete or inappropriate data. At the same time, they are driven by the hope that at least they won't have to wait too long for the data. This is because the existing IT infrastructure is not necessarily prepared for dedicated data queries.
The quality of a data pipeline depends on its flexibility. Traditional pipelines run on premise and use expensive hardware that is difficult to maintain. In addition, their usability is limited due to slow performance. If several workloads are active in parallel, the data flows are sluggish and compete with each other. At peak times, this is an absolute horror scenario and querying real-time data is at best an El Dorado for data dreamers.
Modern data pipelines use the latest cloud technologies and are therefore scalable, agile and dynamic. They respond immediately to increasing or decreasing workloads and answer queries on specified datasets without delay at the time of their request. Cloud-based data pipelines enable business users to carry out self-determined and timely data analyses. Of course, all of this also reduces costs.
Number 2: Self-service thanks to ELT tools and modern data pipelines
Quickly query a special data set during peak loads? Not an option. At this point, business users spend a lot of time passing on their data queries to the IT managers and waiting for output. IT, in turn, first has to record the query and translate it into its own requirements profile - misunderstandings are often inevitable.
However, unobstructed and fast access to data pipelines for everyone and around the clock is the basis for data democratization in a company. Business users should also be able to query all data sources and data formats. Regardless of whether the data is structured or not even close to being transformed. ETL processes in particular not only require the use of extensive external tools. It can also take months for a team of analytics engineers to set up the corresponding processes. Pipelines for special queries often even have to be reprogrammed. This ties up personnel and time resources for an unnecessarily long time.
The advantage of modern data pipelines is the use of an ELT tool. This means that data is extracted and loaded into the target system, usually a data lake or warehouse, before being transformed. With this immediately accessible raw data, business users can then act according to the situation and draw conclusions based on the context.
Number 3: Data in real time AND in a bundle
Which weather report is based on "old" data? Which sales department can wait days or weeks for information about its customers to drive decision-making processes? With rapidly increasing data streams, there is a growing need for real-time data. The Internet of Things, in particular, makes it unimaginable that only delayed responses should be made to collected data. Waiting times of hours or even days are unacceptable. This is because the data must be forwarded and processed immediately.
Near real-time processing is one of the standard tasks of Modern Data Pipelines. The data is transferred live and in full from one system to another. Real-time analysis delivers dynamic reports with data that is rarely more than a minute old.
Modern data pipelines are of course also able to process accumulated data together in batches. Batch processing still makes sense for reports that are queried once a day or once a week, for example. Particularly complex data queries are handled very well with batch queries. In data-driven companies, both variants will certainly be in demand and implemented.
Competitive advantage of modern data pipelines
Due to the current massive switch by companies to cloud-based technologies, the use of modern data pipelines is initially the logical consequence. Even companies that mainly work with batch-based ETL processes will not be able to avoid ELT-based analyses in the long term. Within a modern data stack, they can implement modern pipelines incrementally, initially involving certain data or business areas and approaching the topic step by step.
One thing is clear: modern data pipelines offer a clear competitive advantage because they allow decisions to be made faster and better. Companies can act immediately and take appropriate options. When renewing pipelines, it is important to ensure that they allow for continuous data processing. They must also be dynamic and flexible and be able to be used independently of other tools, pipelines or technical processes. Direct access to data and pipelines, which should also be easy to configure, is ideal. With convenient applications such as Fivetran or dbt, companies are really picking up speed. These tools enable and facilitate work with data pipelines many times over.
