Companies have started realizing the value of data. What is often ignored is the amount of engineering required to make this data accessible.
Data engineering solutions have three core functions.
- Data-Intensive API & Application
- AI & ML projects
- Data warehousing-based business intelligence and reporting
For every use case, the data pipeline varies. It is a difficult task to choose from the pool of tools and technologies. A data engineering ecosystem comprises of these tools. Data engineering services use generally defined frameworks for visualizing data pipelines and the various data engineering tools. Each tool of the data engineering ecosystem belongs to a different category. Let us look at the components of these ecosystems.
The foremost task is to get data into the system. Three main strategies for data ingestion are:
1. Batch ingestion
The first step of the data engineering ecosystem is uploading data files in batch style. It can be done using a basic function of programming languages or using common data-transformation libraries like Spark and Pandas
2. Stream Ingest
It includes high throughput messaging system with computation capabilities. Widely used open-source tools are Kafka & Flink. Other available tools are AWS streaming and Google Pub/Sub.
3. Managed SaaS Ingest
Data ingestion can be done from operational systems like Salesforce CRM, Hubspot account, and internal databases. Rather than fetching data from these tools, use pre-built data connectors for ingestion. Tools available in this category are Segment, Stitch, Fivetran, Snowplow, and Matillion.
Data is stored using data lakes in data science engineering. The data lakes are centralized repositories that store structured and unstructured data. The two main architectures of data storage are:
1. Object storage
The object must efficiently store the data so that the data can be easily consumed by applications present in the data engineering ecosystem. All cloud-based object storage applications such as AWS S3, ADLS, GCS are examples. Other examples are wasabi, Pure, & MinIO.
2. Analytics Engine
These engines provide a SQL interface to the tabular and relational database. Analytics engine like Databricks Lakehouse, Dremio, and BigQuery offers computations only. Some analytical engines of data science engineering like Snowflake, Druid, and Redshift offer storage services with analytical engines.
Metadata is used to define schema, data types, relations to other databases in data engineering services. It helps in improving manageability and in adhering to good practices. Some metadata management formats are:
A. Open table formats
They improve data mutability and maximize performance by giving freedom to store data in user desired format. They achieve it by managing metadata files over the dataset, allowing fast read and write operations. Hudi, Iceberg, and Delta are some tools that give open data formats provision in data engineering.
B. Meta stores
They abstract files in object storage and transform them into a query-able table. Hadoop’s Hive is the sole meta store available. It provides tabular access to the content.
Data pipelines require streamlining of tasks as it includes thousands of jobs where the input of several jobs is dependent on output. Open-source applications like Airflow, Dagster, and Perfect offers orchestration in data engineering services.
Data is present in the data engineering system after data ingestion. It is time to crunch it.
To handle data volumes, distributed compute load is a must. This category has evolved to give near real-time computation through SQL and code interfaces.
1. Distributed computing
It is dominated by Spark, an open-source technology available almost on every cloud provider.
It gives access to data via a single endpoint regardless of the location of data. PrestoSQL was the first one to provide such services. Now, every cloud offers its version of PrestoSQL.
Data Science & Analytics
Certain tools in the data engineering ecosystem are developed to improve business intelligence and data science functions. These data science tools are divided into the following categories:
Interest in the development and maintenance of machine learning models has increased exponentially in the last few years. A dozen of open source tools present are MetaFlow, DisDat, and KubeFlow(Google).
2. Analytics workflows
Analysts face a challenge in the organization and execution of transformational queries. DBT and Dataform have come to the rescue for running data-intensive code/SQL.
Notebooks have become preferred tools for exploratory analysis, ML model training, and production ETL jobs. Jupyter, Deepnote, and Hex are players in this segment.
The tools in this section aim to enhance the usability of data platforms in an organizational context.
The discovery tools help users find datasets easily, visualize connections among them, and see how they are used. At an organization level, it is important to make information readily available to promote a data-driven culture that is efficient and consistent.
2. Manageability and Governance
Organizations are looking for data auditing, reproducibility, and governance in data engineering services. Tools in this segment simplify data management and governance.
3. Quality & Observability
This segment offers rules or ML-based data quality monitoring and testing. Errors and anomalies are likely to occur in data engineering solutions. These tools aim to identify those errors before your consumer finds them out.
We have looked at different segments present in the data engineering ecosystem. Though it might seem hard to implement, it is a future safe and wise decision to use these while finding data engineering solutions.
Please share your thoughts on data engineering at firstname.lastname@example.org, and we can also discuss your business requirements if any.