Mastering Data Engineering: A breakdown of Data Pipeline Stages and Tools
The market is flooded with numerous data engineering tools due to the exponential increase in data volumes. Starting a career as a Data Engineer can be quite overwhelming. This article explores the different stages of a data pipeline and the technology stack associated with each stage. There are both open source tools and cloud managed tools available (such as AWS, GCP, Azure).
To become a successful data engineer, it is important to learn at least one technology from each stage of data processing. These stages include data sources, data ingestion, data processing, data transformation, storing into the warehouse/databased, running analytical queries on the warehouse, and storing the final output into the database (SQL/NoSQL). In this article, we will discuss popular tools used in each stage, as well as the comparison between open source technology and its equivalent cloud managed technology.
Let us embark on our journey exploring languages and SQL. When it comes to languages, it is essential to acquire proficiency in Python, Java (Core java and OOPs), and Scala (OOPs). It is advisable to commence with a fundamental understanding of object-oriented programming concepts initially and then progress to more advanced topics.
Moreover, a solid grasp of SQL is of utmost importance. While there are numerous open source and cloud-specific tools available, the majority of them are compatible with SQL.
Languages : Python, Java, Scala
Scripting : SQL
Few examples of SQL interface supported warehousing tools include hive, Google’s big query, AWS RedShift, Azure synapse, Databricks SQL etc. Regardless of the cloud platform you utilize, writing queries is not a concern as all these tools support SQL. Although there might be slight syntax level variations in the queries, the underlying SQL concept remains unchanged.
After acquiring proficiency languages (at least OOPs) and SQL, you can begin to prioritize obtaining certifications from various cloud vendors, such as the Associate Data Engineer and Professional Data Engineer certifications. You are not required to enroll for all courses. You have the option to choose any cloud vendor based on the current project in your organization. This will allow you to optimize the benefits for both parties.
AWS Certified Data Engineer — Associate
Azure Data Engineer Associate
GCP Professional Data Engineer
Data Ingestion :
Data Ingestion Tools are responsible for —
• Gather data from various sources.
• Facilitate smooth transition into data lakes, warehouses and stream processing engines.
• Handle batch and streaming data, structured, semi-structured, unstructured data.• File System : Data can be stored in distributed file systems or cloud storages. The file system enables fundamental operations like file/object creation, file copying, file moving, file deletion, file renaming, and more. These operations are supported by open source file system HDFS, and there are also cloud-managed alternatives like Amazon S3, Azure blob, and GCP buckets. While there may be differences in syntax between file systems, the usage remains consistent.
• Streaming : An open-source tool like Apache Kafka is available for real-time data ingestion. Similarly, a cloud-managed tool equivalent to Apache Kafka is also provided. The functionality and usage of this tool remain identical to Apache Kafka. The only distinction is that these cloud-managed tools are fully managed by the cloud vendors, allowing us to utilize them as a platform-as-a-service. Consequently, we are relieved from the burden of managing installations and infrastructures.
Data Stores / Data Warehousing / Databases (NoSQL/SQL) :
Data Storage Tools —
• Offer reliable, scalable, secure solutions for large data volumes.
• Essential for seamless integration with storage layer for optimal performance.ETL Role in Data Conversion —
• Converts data into usable format.
• Performs tasks like data validation, cleanup, normalization, aggregation, enrichment.
• Enhances dataset readiness for consumption across landing, raw, and curated zones.Data Warehousing —
• Allows ad-hoc analysis and custom report creation.
• Serves as central repository for current and historical data.
• Provides comprehensive, long-term business information view.
• Empowers informed decision-making based on data trends and patterns.NoSQL Databases -
• Known as “not only SQL” databases.
• Ideal for organizations with large data volumes that cannot be easily fit into a relational model.
• Flexible schema model includes documents, key-value pairs, wide columns, graphs.
• Offers dynamic schema, horizontal scalability, and ease of development.
Data Processing (MPP):
Distributed Data Processing —
• Distributes workload across multiple machines, servers, or nodes.
• Allows concurrent data processing.
• Results in faster processing times and enhanced system performance.There are two types of data processing frameworks: real-time and batch. Apache Spark is an open-source distributed data processing framework. Cloud providers offer alternatives to Spark, such as Amazon Glue, Azure Synapse, and GCP Dataflow, which are fully managed services. There are some enterprise tools available such as confluent which provides SQL based streaming data processing engine which is available on all the clouds such as Azure, CGP and AWS.
Distributed Database with Query Engine:
You don’t actually need a separate data processing framework like Spark. There are few databases available that come with their own processing engine. All you have to do is write your query and hit the run button. It’s important to note that many distributed databases typically don’t support OLTP and are mainly designed for executing analytic queries (OLAP).
Guess what? The learning curve for most of these databases/data warehouses is practically zero because they all support SQL interface. How cool is that? The only thing you really need to focus on is getting familiar with each tools unique architecture and best practices. But don’t worry, it’s all part of the fun!
Here are few example :
Apache Druid : open-source, SQL based , optimized for big databases.
Apache Postgres : open-source relational database management system emphasizing extensibility and SQL compliance.
Apache Timescale : SQL based. Additional SQL functions and table structures provide support for time series data oriented towards storage, performance, and analysis facilities for data-at-scale.
Apache Clickhouse : SQL based, OLAP supported, Generates real-time analytical reports.
Snowflake: SQL based, OLAP supported. its enterprise tool which is available on almost all cloud platforms like AWS, Azure and GCP.
Confluent kStream Apps and ksql : streaming SQL engine for Apache Kafka. providing a simple and completely interactive SQL interface for processing data in Kafka.
Data Cache
• Enhances data retrieval performance by reducing access to slower disk storage layer.
• Prioritizes speed over capacity in data storage.
• Temporarily stores a portion of data.
• Benefits companies of all sizes by enhancing application performance.
Data Catalog :
A data catalog serves as a centralized inventory of data assets and provides information about those assets. Its purpose is to enable organizations to efficiently find and comprehend data. However, data catalogs have the potential to offer the modern enterprise a more effective means of utilizing their data for analytics and artificial intelligence (AI) initiatives.
Data Asset Management -
• Identifying data assets across databases, data lakes, and systems.
• Organizing information about organization’s data.
• Determining relevant and suitable data for use.
Data security and governance tools :
• Data security and governance tools protect the data in the storage layer and the processing resources in other layers.
• Provide mechanisms for encryption, access control, network protection, usage monitoring, and auditing.
• Security layer monitors the activities of all components in other layers and generates a detailed audit trail.
• Components of all other layers must integrate with the security and governance layer.
Data Orchestration :
The benefits of orchestration tools are :
• workflow management
• Automating data workflowsFew popular Data orchestration tools are :
• Apache Ozzie
• Apache Airflow
• Azkaban
Summary :
If you’re eager to become a super successful Data Engineer, whether you’re just beginning or already rocking it in the field and aiming for those Data Architect roles, boy, do I have an amazing recommendation for you! Get ready to jumpstart your journey and brace yourself for some serious excitement.
First things first, let’s talk about learning Python, Java, Scala, and SQL. Trust me, once you’ve mastered these bad boys, you’ll be well on your way to reaching for the stars. there’s more! It’s time to explore the incredible world of tools in the pipeline. Picture this: Apache HDFS, Kafka, Spark, Hive, HBase, Redis Cache, and Oozie/Airflow.
But hold on tight, How about diving headfirst into the fascinating realm of cloud technology stack with certification? Oh yes, my friend, we’re talking about taking your skills to new heights and unleashing your true potential. The possibilities are endless!
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