Data Engineering Tools to Know: What Each One Is For

The data engineering tools worth knowing are best learned by the job each one does in a Data PipelineData PipelineA set of steps that moves data from where it is made to where it is used, cleaning and reshaping it on the way, run on a schedule or as data arrives.: moving data in (Fivetran, Kafka), storing and processing it (Spark, Databricks, Snowflake, BigQuery, Microsoft Fabric), transforming it into tables people use (dbt), and running every step on schedule (Airflow, Prefect). Most teams use one tool from each group, not all ten. This guide explains what each is for, what has changed recently, and how to choose.

Moving data in

Fivetran copies data from SaaS applications, databases and files into your warehouse or lakehouse with ready-made connectors. It loads changes incrementally and adjusts the destination tables when the source adds a column, so nobody writes or maintains extraction code. You pay by the volume of rows that change. In 2025 Fivetran and dbt Labs, the company behind dbt, merged; both have said dbt's open-source projects keep their licences.

Apache Kafka is the standard for streaming events between systems: a durable, ordered log that many producers write to and many consumers read from, at high throughput. Kafka 4.0, released in March 2025, was the first version to run entirely without Apache ZooKeeper, using its own KRaft mode instead, which removes a whole separate cluster from every installation. Use Kafka when data must arrive in seconds, for example orders, clicks or sensor readings, and a nightly batch is too late.

Storing and processing

Apache Spark is the open-source engine for processing data across many machines, in batch and streaming, from Python, SQL, Scala, Java or R. Spark 4.0 turned on ANSI SQL by default and added a lightweight Python client; Spark 4.1 added Spark Declarative Pipelines. See what's new in Spark 4.0.

Databricks runs Spark as a managed platform on AWS, Azure and Google Cloud, with Delta Lake tables, Unity Catalog for permissions and lineage, SQL warehouses, notebooks, jobs and pipelines, and machine learning and AI tools. It is the usual choice for a LakehouseLakehouseOne store for all of an organisation's data: cheap files in cloud storage, as in a data lake, with the tables, transactions and fast queries of a data warehouse on top., where raw files and analytics tables live in the same cloud storage. Our Databricks cost calculator prices clusters and warehouses by cloud and region.

Snowflake is a cloud data warehouse that separates storage from compute: each team runs its own virtual warehouse and pays per second while it runs. It handles semi-structured data such as JSON, shares data across accounts and clouds, and can also read and write Apache Iceberg tables in your own storage. The Snowflake cost calculator estimates a monthly bill, and Snowflake vs Databricks prices the same SQL work on both.

Google BigQuery is Google Cloud's serverless warehouse: there are no clusters to size, you load data and query it, and pay for the data scanned or for reserved capacity. BigQuery ML trains and runs models with SQL. It is the natural choice for teams already on Google Cloud.

Microsoft Fabric brings data engineering, warehousing, real-time analytics and Power BI into one service, with OneLake as a single store for all of it. Microsoft's data engineering certification now covers Fabric rather than the separate Azure services such as Synapse Analytics (see DP-203 is retired).

Underneath many of these sit open table formats, Delta Lake and Apache Iceberg. They turn folders of Parquet files into tables with transactions, schema changes and time travel, so several engines can read the same data without copying it. When you compare platforms, check which formats each can read and write.

Transforming

dbt turns SQL SELECT statements into tested, documented tables and views in your warehouse. Each model is a file in version control, dbt works out the order to build them from their references to each other, and tests (unique, not null, accepted values, relationships) run with every build. It is how most analytics teams apply software practices to the transformation layer, often in a Medallion ArchitectureMedallion ArchitectureOrganising a lakehouse in 3 layers of rising quality: bronze for raw data, silver for cleaned and joined data, and gold for tables shaped for reports and AI. of raw, cleaned and business tables. dbt Core is open source; dbt's newer Fusion engine and the hosted dbt platform add speed and a managed service.

Running it all on schedule

Apache Airflow is the most widely used orchestrator: you write each pipeline as a Python DAG (a graph of tasks and their dependencies), and Airflow schedules, retries and monitors it. Airflow 3.0, released in April 2025, was its biggest release since 2.0, with DAG versioning, a new interface, and tasks that can run outside the main cluster.

Prefect is a Python-first alternative: you decorate ordinary Python functions as flows and tasks, and it handles retries, caching, scheduling and a monitoring interface, either self-hosted or in Prefect Cloud. Teams that find Airflow heavy for a few dozen Python pipelines often choose it.

Databricks Lakeflow Jobs, Fabric pipelines and Snowflake tasks also schedule work inside their own platforms. A separate orchestrator earns its place when a pipeline crosses several systems.

How to choose

Two questions narrow the choice faster than feature lists: which cloud you are already on, and whether your team works mainly in SQL or in Python. A small team with mostly SQL work is usually well served by a warehouse, a managed ingestion tool and dbt. A team with large or streaming data, or machine learning, tends toward Spark on a lakehouse.

Tecyfy Takeaway

Learn the tools by their jobs: ingest, store and process, transform, orchestrate. Spark and SQL are the skills that carry across almost all of them, and open table formats are making the storage layer shared between engines. Pick one tool per job that fits your cloud and your team's language, and add the rest only when a real need appears.