As we emerge from the impacts of COVID-19, the landscape for data analytics and data science projects has drastically shifted. Societal changes, including fluctuating financial landscapes and leading advancements, have made it necessary to adapt and scale data platforms. Additionally, the rise of streaming data and the importance of data orchestration have become paramount in managing the vast amounts of data both large corporations and small startups now handle, equivalent to levels previously only seen by giants such as Netflix, Uber, and Spotify.
No singular vendor currently dominates the data field. However, companies such as Snowflake and Databricks have made notable advancements. The integration of data platforms has become more refined, this split into three categories; Batch, Streaming and Eventing. These are crucial to manage incoming data from various sources at different intervals, handle data at the speed of business, and generate first-party data. A robust example of data integration is Fivetran, a leader in the managed ETL category.
The shift from “Data Warehouse” to “Data Store” acknowledges the rise of Data Lakes. This strategy emphasizes utilizing them as a staging area for both structured and unstructured data. Tools including Presto and Trino traverse Data Lakes, facilitating insights from raw data. Additionally, specialized data stores like vector databases gain relevance in light of the AI and Large Language Model trends.
Dbt, a transformative data engineering tool, is the standard for organizations seeking to automate data transformation across their platform. Stream transformations, though less mature, are essential for advancing the implementation of streaming data. Key technologies poise to drive adoption of these include Flink SQL and managed services from providers like Confluent, Decodable, Ververica, and Aiven.
Data orchestration has become essential in choosing the correct set of tools, technologies, and solutions. Airflow remains dominant in this area. Orchestration not only streamlines the building process but also yields scalable solutions, a crucial factor in enterprise growth.
The presentation of data, surprisingly, continues to be dominated by traditional platforms such as Tableau, PowerBI, Looker, and Qlik. Yet newcomers like Sigma, Superset, and Streamlit augur well for the future. Transportation, or data activation, is critical to feeding valuable transformations back into the business’s core systems and processes, impacting operations significantly.
Data observability has also emerged as a major component of a successfully scaled data platform. Tools such as DataHub and Monte Carlo play crucial roles in metadata consolidation and observation. Going forward, modularity and the ability to manage data of varying magnitudes will be key to building platforms suited for present needs and adaptable to future demands. This evolution offers unprecedented opportunities to adopt innovative solutions and solve problems with data.
