Data Pipeline: Purpose, Types, Components and More

What is a data pipeline?

A system that manages the delivery, storage, and processing of data is called a data pipeline . Data pipelines can be used for a variety of purposes, but they are mostly employed to extract insights from massive amounts of raw data. Using a pipeline has several advantages, such as quicker processing times, more scalability for adding new datasets, and more security for the data that is stored. We will go over the definition of a pipeline, its various varieties, common parts, and the benefits of using the system in this blog post.

A data pipeline: what is it?

A data pipeline is a set of operations carried out on data. These processes may involve transforming, cleaning, aggregating, and tidying to prepare the data for analysis or modeling.

One could be used by a data scientist as a component of an ETL processor to prepare the data for analysis. In this manner, they may solve important business problems without wasting time constructing themselves.

Data exploration: To ensure your dataset is error-free, you must evaluate and clean up the data before you can begin building models or doing analysis. When modeling or analyzing, this can save time because there won't be any "bad" data points that could produce unreliable conclusions. Additionally, it guarantees clean output for reporting needs, guaranteeing accurate numbers for readers of reports based on this data.

Transparency: Having a clear process for how specific types of data must be handled before being used by different teams is important when working with multiple analysts within an organization. This makes it possible for everyone to understand precisely what actions were taken on the dataset and what kinds of presumptions they should have while using the data.

Governance: Ensuring no losses or unauthorized changes to the dataset can be achieved by using a standard data management procedure.

There are many kinds of pipelines

Basic datasets

Simple fields like names, addresses, and phone numbers are typically included in this kind; these fields don't need to be cleaned up to be utilized in reporting or analysis.

Intermediate-sized dataset

It contains more intricate datasets, many of which include numerous tables with a wide variety of attributes (e.g., name, address, email, etc.) within each table. Moreover, these datasets could have a variety of dependencies, such as the necessity for a field from a separate database for one characteristic to properly populate for another.

Intricate dataset

This category comprises datasets with millions of entries, many of which need to go through many processing stages (such as feature extraction, data quality checks, and so forth) before they can be utilized for reporting or analysis.

Advantages of employing a data pipeline

Processing velocities

Because a data pipeline removes the need to wait until every step is finished before analysis, it makes data processing much more efficient.

Security

By handling everything on a single platform rather than having different users work on separate components and then attempt to merge them, data security is significantly enhanced.

Control of access

Because each user may only examine the datasets and tasks they are assigned, there is less chance that confidential information will be disclosed to unapproved parties.

Collaboration

Using a data pipeline, several people can work on separate projects without running into each other's way or overlapping their work. This helps users save time by quickly interpreting the work of others before beginning their analysis.

Overall effectiveness and flexibility

Teams inside an organization can collaborate more effectively by sharing resources like real-time datasets, code libraries, and reusable parts (like Part-of-Speech tagging) through data pipelines . The time that would have been spent building the pipeline is greatly reduced for users. It's quite simple to use, and you won't have to worry about making as many mistakes as you could while creating your own.

Components of the data pipeline

Data pipelines can consist of different kinds of components, each with its own set of technological needs and implementation difficulties.

A typical data pipeline's general structure would resemble this:

Destination

The first thing to think about is the destination. It indicates why and where the data is needed. Pipelines often end with data stores, also known as data warehouses , data lakes, data marts, or lake houses. Pipelines may also go to applications to handle operations like implementing and training machine learning models, among other things.

Origin

The design of the data store frequently influences origin considerations. When optimizing transactional performance and storage costs or enhancing latency for pipelines operating in close to real-time, data should be where it makes the most sense. If transactional systems deliver the accurate and timely information required at the pipeline's destination or destinations, they should be regarded as an origin.

Dataflow

The series of procedures and repositories that data passes through on its way from a source to an endpoint is known as dataflow. Dataflow is a component of the comprehensive pipeline design process, which takes a lot of thought into account.

Storage

Storage includes both the data saved at pipeline endpoints and the systems where intermediate data is kept throughout the pipeline. Relational databases, columnar databases, document databases, graph databases, key-value stores, and so forth are examples of storage possibilities. The storage format and structure, the length of data retention, the data's intended uses, and other factors are often determined by the volume of data.

Processing

The actions taken to acquire, transform, and transfer data across the pipeline are referred to as processing. Processing is the conversion of input data into output data by carrying out the appropriate steps in the correct order. Data is improved, enriched, and formatted for certain intended uses through ingestion operations, which export or extract data from source systems. A few more typical data pipeline procedures are those involving mixing, sampling, and aggregation.

Workflow

The series of procedures and data storage is referred to as the workflow. Sequencing and dependencies are managed by pipeline workflows at two different levels: the level of individual tasks carrying out a particular function, and the level of units or jobs comprising several activities. Pipelines allow for far smaller batch sizes of data to pass through than when streaming from real-time sources. To move from source to endpoint or destination, some pipelines may just have one task, while others may have numerous tasks coupled with dependencies and data transformations.

Monitoring

Monitoring entails keeping an eye on a data pipeline to guarantee robust performance, dependability, and efficiency. When building pipeline monitoring systems, factors to take into account are: what needs to be monitored; who will monitor it; which thresholds or limits apply; and what will happen when these limits or thresholds are achieved.

Alerting

Data teams are notified by alerting systems when events in a pipeline arise that call for action. Email and SMS alerts are examples of alerting systems.

Infrastructure and tools for data pipeline

A data pipeline is an assembly of components and systems that facilitates the efficient management of datasets and the conversion of data into useful insights. The ultimate objective is to make better company direction decisions by utilizing the data included in these databases. Nine key components make up an effective data pipeline :

Schedulers for batches

Scheduled operations to send emails, run scripts, copy data between databases, and so forth.

Data lakes

It could be required to provide an unstructured file structure when using a data lake to store and process data for usage in the pipeline.

Determining metadata attributes that can subsequently be searched against text values using regular expressions (regex) or SQL queries is one way to go about it. Alternatively, all of this data may be included in file-naming conventions, which would tell scripts searching for particular kinds of structured or unstructured items where to seek.

With flexible scripting and metadata management solutions such as Lyftrondata, customers may import their key-value metadata tags from relational databases without writing any code.

Data warehouse

Data warehouses are frequently used as the last stop in data pipelines, storing aggregated, summarized, and converted datasets.

Applications for ETL

Data pipelines can be created using a variety of commercial and open-source ETL technologies that are readily accessible on the market.

Languages used in programming

Programming languages that are easily understood by the script writer must be used by ETL developers. For instance, SQL is frequently used to write pipeline tasks because it simplifies data translation and can be installed on top of pre-existing relational databases without requiring the installation of a separate database. However, they can be used to mix any kind of data—structured, unstructured, or semi-structured—and communicate with different databases, Other languages, like Python , are gaining popularity.

Tools for validating data

Several commercial and open-source data validation tools and frameworks are available to verify whether the transformed dataset satisfies organizational standards. For instance, it could have to abide by legal requirements like HIPAA or ISO.

Tools for concealing data

Running a data masking tool on the source datasets is frequently required throughout the data validation process to remove sensitive information before transferring it into a new dataset. As a result, no real names or numbers are revealed, and users may test their processes using data that is safe to use yet realistic.

Kerberos identity management

To ensure that all system access is approved and encrypted and that passwords are never sent across the network in plain-text format, Kerberos authentication can be utilized for the maximum level of security. Even if unauthorized individuals can obtain access, this keeps them from accessing any portion of a production or development environment.

Instantaneous data access with the Lyftrondata columnar ANSI SQL pipeline

Numerous well-known businesses have spent millions of dollars manually creating data pipelines, but regrettably, they were unable to get a return on investment. The end consequence has primarily been a complicated ecology driven by data that costs a lot of money, time, and labor to manage.

With its Columnar SQL data pipeline, Lyftrondata eliminates all of these distractions and provides enterprises with a consistent, integrated flow of data for analysis, discovery, and decision-making. Without having to worry about manually building data pipelines , users may transition from legacy databases to a modern data warehouse and instantly access all the data from various areas in a data hub.

Using analytics and BI tools, Lyftrondata's columnar pipeline imports the data into the target data warehouse from all data sources into a single format. To ensure that the appropriate data is available when needed, use Lyftrondata's automated pipeline rather than reinventing the wheel by creating pipelines by hand.

How it functions

With a few simple commands, users may analyze and load events from various sources into target data warehouses using Lyftrondata's columnar pipeline. SQL is used by Lyftrondata to define all of its data pipelines . Because of this idea, all data pipelines may be scripted, negating the need for manual construction. Data pipelines could be constructed automatically by scripting rather than by a graphic designer by hand. Make sure that you can use any BI tool of your choice to sync and view your real-time data in a matter of seconds.

Use Lyftrondata Source to understand the inner workings of the data pipeline

An intimate peek of the columnar process of Lyftrondata

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