BigQuery Trends

RamenApps Analysis

BigQuery is the party animal of the data world - always in the center of attention, never shying away from a challenge. Known for its ability to manage and analyze huge amounts of data, BigQuery has attracted a flock of equally impressive guests to its trendy category. Databricks, Terraform, Data Pipelines, Data Lake, Looker Studio, Airbyte, Big Data Engineers, Firebolt, Airflow, Data Lineage, Matillion, Doubleclick, Crashlytics, Data Pipeline, Cloud Monitoring, and Data Catalog - all hungry for a taste of BigQuery's exclusive services. These trends speak to the growing need for organized, comprehensive management of data and the desire to harness its power. So, if you're looking for a good time with data, BigQuery is your go-to - and all the cool kids are there.

Related tags to BigQuery

5 Years

Monthly Searches

1

Software company

Databricks is an enterprise software company that combines data warehouses and data lakes into a lakehouse architecture. It was founded by the creators of Apache Spark and provides a web-based platform for working with Spark, offering automated cluster management and IPython-style notebooks. Databricks is used for processing, storing, cleaning, sharing, analyzing, modeling, and monetizing datasets, with solutions ranging from business intelligence to machine learning. It is available on two cloud platforms, Azure and AWS, and is infinitely scalable and cost-effective. The Databricks platform can handle all types of data and everything from AI to BI, making it popular among data scientists and data engineers.

Monthly searches:
90,500
Competition:
LOW

Looker Studio is a business intelligence tool used to turn data into visual reports and dashboards that are easy to read and share. It is a free tool that can be used to automate processes with data regardless of where the data is stored. Looker Studio was formerly known as Google Data Studio, which was officially rebranded in October 2022. Looker is now the name used for Google Cloud business intelligence products, which includes Looker Studio, Data Studio, and other core Google technologies like artificial intelligence and machine learning.

Monthly searches:
27,100
Competition:
LOW

Airbyte is an open-source data integration platform that consolidates data from applications, APIs, and databases to warehouses or other destinations. It is an ELT (Extract, Load, Transform) tool with a modular architecture and a growing community of developers who update connectors as source APIs and schemas evolve. Airbyte has a built-in scheduler and uses Temporal to orchestrate jobs, ensuring reliability at scale. Airbyte leverages dbt to normalize extracted data. It is a safe and self-hosted solution that runs in the safety of your cloud. It makes extracting data from various sources super easy, freeing up time to generate meaningful insights from data. Airbyte has been funded by Y Combinator.

Monthly searches:
6,600
Competition:
LOW

Data pipelines are a series of tools and processes designed to automate the flow and transformation of data from a source to a destination. These destinations may include data warehouses, data lakes, analytics databases, and other repositories. The process of data pipeline involves ingesting the raw data from various sources and then transforming, validating, and loading it into a target system. ETL (Extract, Transform, Load) is a type of data pipeline that involves the process of extracting data from various sources, transforming it in some way to make it suitable for analysis, and then loading it into a destination system. AWS Data Pipeline is a popular web service that automates the movement and transformation of data. There are many other types of data pipelines that can be used depending on the specific needs of an organization. Overall, data pipelines are essential for organizations that need to move and transform large volumes of data quickly and efficiently. They allow businesses to gain valuable insights from their data in a timely manner, ultimately helping them make better decisions based on that information.

Monthly searches:
5,400
Competition:
LOW

A data catalog is an organized inventory and detailed list of all data assets in an organization that helps manage and discover data. It uses metadata management to enable data analysts, scientists, stewards, and other data consumers to find and understand datasets for extracting business value. It includes data from the World Bank's microdata, and open-source data catalog tools. Some examples of data catalog tools are Amundsen by Lyft and LinkedIn DataHub. The difference between a data catalog and a data warehouse is that the former helps find, understand, trust, and use data, while the latter stores structured data.

Monthly searches:
3,600
Competition:
MEDIUM

1