Modern data pipeline tools
How modern technologies optimize data analysis resources
For data-driven companies, functioning data pipelines are essential. Data pipeline tools such as Fivetran or dbt reduce complexity and maintenance costs to build data pipelines reliably and independently without leaks. Data analysts in particular benefit from this.
Data analysis is a highly dynamic matter. Data is extracted, transformed, combined, validated, and loaded. Data pipelines not only ensure automated processes. They also keep moving data stringent and consistent. With data pipelines, companies ensure the professional preparation and preparation of their data. So-called data ingestion, i.e. the connection of data, is an important basic component within the modern data stack and requires a reliable structure.
The data pipeline as a production line
What are the reasons why companies use data pipelines? The following analogy describes motivation very well. There are various production processes in industry, including so-called series production in the area of manufacturing. Different products and building materials are combined together in a production line. Initially, it was trained specialists who took care of production and processing. Henry Ford further developed these production lines and provided 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 employees. The respective work step could now also be carried out by those who did not need to be trained for the specific task, but were above all experienced in using the machines. The techniques and sequences, in turn, have been continuously developed. An efficient and scalable business model.
Tool-based or self-made?
Modern data pipelines are nothing more than automated and sequential processes within a production line. They process the data and store it at a central, outsourced location, such as a data lake or data warehouse. Even when real-time or sophisticated data analyses are required or the fully automated storage of data in the cloud is desired, data pipelines are an irreplaceable tool. Without them, most companies will not be able to conduct valid data analysis. The question is therefore no longer whether data pipelines should be set up, but how this can be done with which resources.
In the past, data was extensively provided by ETL pipelines developed in code. But setting up and maintaining your own data pipelines internally is a complex process. First, a method for monitoring incoming data must be developed. Then there is a need to connect to each source and transform data so that it matches the format and scheme of the destination. Data must go to a destination database or to a Data warehouse be postponed. When business requirements change, it is necessary to add and delete fields and change entire schemes. It is also necessary to set up database modelling, including transformations. Last but not least, a data team is faced with an ongoing, permanent commitment to maintaining and improving the data pipeline and interfaces.
Data pipeline tools relieve engineers and empower analysts
These processes are costly, both in terms of resources and time. There will be experienced and therefore expensive personnel from the sector Analytics Engineering requires, which must either be discontinued or trained and withdrawn from other projects and programs. It can take months to set up, resulting in significant opportunity costs. Last but not least, these types of solutions do not always scale, so that additional hardware and employees are required, which quickly costs the budget. Building your own data pipelines usually only makes sense in exceptional cases and under certain conditions.
Today, technologies and data pipeline tools also enable data analysts to independently build high-quality pipelines after a short training period, which is an excellent solution, especially for recurring requirements. Analytics engineers are also relieved and use their resources on more complex project requirements. Dealing with data pipeline tools such as dbt or Fivetran is easy to learn with basic know-how in the areas of data connectivity and analytics — in the spirit of Henry Ford.
Three good reasons for modern data pipelines
Number 1: Cloud flexibility
Business users generally need data on demand. However, time-consuming and sometimes even nerve-wracking IT inquiries are the order of the day. These are often associated with the fear of receiving incomplete or inadequate data. At the same time, they are driven by the hope that they will at least not have to wait too long for the data. This is because the existing IT infrastructure is not necessarily prepared for specific data queries.
The quality of a data pipeline depends on its flexibility. Traditional pipelines run on premise and use expensive hardware that is complex to maintain. In addition, their usability is limited due to sluggish performance. If several workloads are running in parallel, the data flows run sluggishly 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 spaces.
Modern data pipelines use current cloud technologies and are therefore scalable, agile and dynamic. They respond immediately to increasing or decreasing workloads and answer queries on specified datasets immediately 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 entails cost-reducing aspects.
Number 2: Self-service thanks to ELT tools and modern data pipelines
Do you want to quickly query a special data set during peak loads? Bad news. At this point, business users spend a great deal of time passing on their data request to IT managers and waiting for output. IT, in turn, must first take up the request and translate it into its own requirement profile — misunderstandings are often inevitable.
However, unobstructed and fast access to data pipelines for everyone and around the clock is considered the basis for data democratization in a company. In addition, business users should be able to query all data sources and data formats. Regardless of whether it is structured or not yet nearly transformed data. ETL processes in particular require not only the use of extensive external tools. It can also take months for a team of analytics engineers to set up appropriate processes. Pipelines often even have to be reprogrammed for specific queries. As a result, personnel and time resources are tied up for an unnecessarily long time.
The advantage of modern data pipelines is the use of an ELT tool. Data is therefore extracted and loaded into the target system, usually into a data lake or warehouse, before it is transformed. With this immediately accessible raw data, business users can then act on a situational basis and make context-related conclusions.
Number 3: Data in real time AND in bundle
Which weather forecast is based on “old” data? Which sales department can wait days or weeks for information about their customers to drive decision-making processes? With rapidly increasing data flows, there is a growing need for real-time data. The Internet of Things in particular makes it inconceivable that only time-delayed responses should take place to collected data. Waiting times of hours or even days are unacceptable. This is because the data must be forwarded and processed immediately.
So-called near real-time processing is one of the standard tasks of modern data pipelines. The data is transferred in full and live from one system to the other. Real-time analysis provides dynamic reports whose data is rarely older than a minute.
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 a week, for example. Particularly complex data queries are handled very well with the batch query. In data-driven companies, both variants will certainly be in demand and will be implemented.
Modern Data Pipelines Competitive Advantage
Due to the current massive conversion of companies to cloud-based technologies, the use of modern data pipelines is initially the logical consequence. Even companies that mainly work with batch processing ETL processes will not be able to avoid ELT-based analyses in the long run. Within a modern data stack, they can implement modern pipelines incrementally, first involve specific data or business fields and thus approach the topic piece by piece.
One thing is clear: Modern data pipelines offer a clear competitive advantage because decisions can be made faster and better with them. Companies can act immediately and take appropriate options. When renewing pipelines, ensure that they allow continuous data processing. Furthermore, they must be dynamic and flexible and can 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 steam. This is because these tools make working with data pipelines much easier.
