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At the right time, the provision of the right data is the basic requirement of any organization in the data -driven society. But let’s be honest: creating a reliable, expanding and maintaining data pipeline is not an easy task. It requires a combination of planning, deliberately designing, and business knowledge and technical skills. Whether it is connecting a number of data sources, managing data transfer, or ensuring timely reporting, each component offers its challenges.
That is why today I want to highlight what the data pipeline is and discuss a very important component of the building.
What is the data pipeline?
Before trying to understand how to deploy the data pipeline, you should understand what it is and why it is important.
Data pipeline is a systematic set of processing steps designed to convert raw data into a useful, analyzing form for business intelligence and decision -making. In straight terms, it is a system that collects data from different sources, changes, enriching and improving it, and then brings it to one or more target places.
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It is a common misconception to equalize the data pipeline with the movement of data in any form. Just moving raw data from point A to point B (for example, for replication or backup) does not create a data pipeline.
Why describe the data pipeline?
There are numerous reasons to explain the data pipeline when working with data:
- Modeviation: Repeat stages for easily maintenance and scalebursts
- Error tolerance: can be recovered from errors with logging, monitoring and recruitment procedures
- Data Quality Assurance: Confirmation of data for integrity, accuracy and consistency
- Automation: Manual interference, while minimizing, runs on a schedule or trigger
- Security: Access protects sensitive data with control and encryption
Three basic components of the data pipeline
Most pipelines are built around ETL (Squeeze, change, load) Or ellit (Squeeze, load, change) Framework. Both follow the same principles: Effectively processing large amounts of data and ensuring that it is clean, permanent and ready for use.
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Let’s break every step:
Ingredients 1: Data Injection (or squeeze)
The pipeline begins by collecting raw data from numerous data sources such as database, APIS, cloud storage, iOT devices, CRMS, flat files and more. Data batches (hourly reports) or real time streams can be reached as (direct web traffic). Its key goals are to securely and reliablely connect with diverse data sources and collect data in motion (real time) or in rest (batch).
There are two common points:
- Beach: Schedule periodically bridges (daily, hour)
- Streaming: Use tools such as Kafka or event -powered APIs to make data permanent.
The most common tools to use are these:
- Batch Tools: Airbet, Fyotrans, Apache Nafi, Customs Auxer/SQL Scripts
- APIS: For data -created data (Twitter, Urostat, Trap Advisor)
- Web Scripping: Tools such as beautiful soup, scripped, or nine -code abrasive
- Flat Files: CSV/Excel from official websites or internal servers
Component 2: Data processing and changes (or changes)
Once it is eaten, raw data should be improved and prepared for analysis. This includes cleaning, standardizing, merging datases, and applying business logic. Its key goals is to align the data with data quality, consistency, and use and analysis models or reporting requirements.
During this second component, multiple measures are usually considered:
- Cleaning: Handle the lost values, remove copies, unite formats
- Change: Apply filtering, deposit, encoding, or new shape logic
- Verification: Check integrity for accuracy guarantee
- Integrate: combine datases from multiple systems or sources
The most common tools are included:
- DBT (Data Blood Toll)
- Apache spark
- Uzar (Pandas)
- SQL -based pipelines
Components 3: Delivery of data (or load)
The changed data is delivered to its final floor, usually data warehouse (for structured data) or data leak (for semi or non -imposed data). It can be sent directly to dashboards, APIS, or ML models. Its key goals are to store the data in a form that supports rapidly queries and scales and enables real time or near -time access to decision -making.
The most famous tolls are included:
- Cloud Storage: Amazon S3, Google Cloud Storage
- Data Warehouse: Big Query, Isnophilic, Data BRICS
- Two Ready Outpots: Dashboards, Reports, Real Time Apis
Six steps to create a data pipeline at the end end
Creating a good data pipeline usually involves six important steps.
Six steps to create strong data pipeline | Picture by the writer
1. Explain the targets and architecture
A successful pipeline begins with a clear understanding of its purpose and the architecture needed to support it.
Key questions:
- What are the basic goals of this pipeline?
- Who are the last user of the data?
- How much fresh or real time does the data need?
- Which tools and data models improve our needs?
Suggested steps:
- Business questions clarify your pipeline will help answer
- Create a high -level architecture diarrhea to align technical and business stakeholders
- Choose tools and design data models accordingly (such as, a star scheme for reporting)
2. Data
Once the goals are explained, the next step is to identify the sources of the data and determine how to eat the data reliably.
Key questions:
- What are the sources of data, and in what form are they available?
- Should the needle be in real time, in batches, or in both?
- How do you ensure data completion and consistency?
Suggested steps:
- Source sources such as APIs, database, or third -party tools, create expansion contacts.
- Use Injection Tools such as airbat, phytrey, kafka, or custom connector.
- Enforce basic verification rules to catch initial errors.
3. Data processing and changes
With the flow of raw data, it is time to make it useful.
Key questions:
- What changes are needed to produce DATA data of analysis?
- Should the data be rich in external inputs?
- How will a duplicate or wrong record be handled?
Suggested steps:
- Apply changes such as filtering, collecting, standardizing, and joining datases
- Enforce business logic and ensure schemes in the tables
- Use tools such as DBT, spark, or SQL to manage and document these steps
4. Data storage
Next, analyze and reporting how and where you store your processed data.
Key questions:
- Should you use a data warehouse, data leak, or hybrid (leakhouse) approach?
- What are your requirements in terms of cost, scalebuability, and access control?
- How will you create data for effective inquiries?
Suggested steps:
- Choose a storage system that is in accordance with your analytical requirements (eg, Big Corey, Asnophilic, S3 + Ethana)
- Design schemes that improve use of use matters
- Plan for data life cycle management, including archive and cleansing
5. Orchestration and automation
All ingredients require workflow orchestration and monitoring to tie together.
Key questions:
- Which steps depend on each other?
- What should happen when one step fails?
- How do you monitor, debug and maintain your pipelines?
Suggested steps:
- Use archetypes such as airflow, prefect, or dugster to make workflose and automatic
- Set up policies and alerts to try again for failures
- Create your Pipeline Code and reuse LID modular
6. Reporting and Analytics
Finally, provide the price by exposing the insights to the stakeholders.
Key questions:
- Analysts and business users will use tools to access data?
- How often should dashboards update?
- Which permission or governance policies are required?
Suggested steps:
- Connect your warehouse or lake with BI tools such as Lokar, Power BI, or Tablo
- Settlement layers or feedback to simplify access
- Monitor dashboard use and refresh performance to ensure the ongoing price
Results
Creating a full data pipeline is not only about data transfer but also about empowering people who need to make decisions and take action. This systematic, six steps will allow you to build pipelines that are not only effective but flexible and expanding.
Each step of the pipeline – Intention, Change and Delivery – plays an important role. Together, they create a data infrastructure that supports data -driven decisions, improves operational performance, and promotes new ways for innovation.
Jozep Ferrer Barcelona is an analytical engineer. He graduated in physics engineering and is currently working in the data science field applied to humanitarian movement. He is a part -time content creator focused on data science and technology. Joseph writes everything on AI, covering the application of the explosion at the field.