The global data catalog market is experiencing steady growth, driven by the increasing volume and complexity of enterprise data. As organizations face the challenge of managing multiple data sources and ensuring data quality and governance, the adoption of data catalogs has become increasingly important. According to market research, the total value of the market in 2025 was approximately $2.61 billion, with a projected CAGR of 2.50% from 2025 to 2033. This growth is primarily attributed to the growing need for data-driven decision-making and the proliferation of big data and artificial intelligence (AI) technologies. Key industry trends indicate a growing emphasis on cloud-based data catalog solutions, as well as the integration of AI and machine learning (ML) capabilities. These technologies enhance the automation and efficiency of data cataloging processes, while providing advanced features such as data lineage tracking and data quality monitoring. Furthermore, the convergence of data catalog solutions with other enterprise applications, such as data governance and data analytics platforms, creates opportunities for comprehensive data management and improved data utilization. The competitive landscape is characterized by a mix of established vendors and emerging players, with companies such as Tamr Inc, Collibra NV, TIBCO Software Inc, and IBM Corporation holding significant market share. Ongoing innovations and strategic acquisitions are shaping the market dynamics, as vendors strive to differentiate their offerings and meet evolving customer requirements. The global data catalog market size was valued at USD 2.0 billion in 2022 and is expected to expand at a compound annual growth rate (CAGR) of 24.3% from 2023 to 2030, reaching USD 12.0 billion by 2030. Recent developments include: November 2022 - Amazon EMR customers can now use AWS Glue Data Catalog from their streaming and batch SQL workflows on Flink. The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog. With this release, Companies can directly run Flink SQL queries against the tables stored in the Data Catalog., September 2022 - Syniti, a global leader in enterprise data management, updated new data quality and catalog capabilities available in its industry-leading Syniti Knowledge Platform, building on the enhancements in data migration and data matching added earlier this year. The Syniti Knowledge Platform now includes data quality, catalog, matching, replication, migration, and governance, all available under one login in a single cloud solution. It provides users with a complete and unified data management platform enabling them to deliver faster and better business outcomes with data they can trust., August 2022 - Oracle Cloud Infrastructure collaborated with Anaconda, the world's most recognized data science platform provider. By permitting and integrating the latter company's repository throughout OCI Machine Learning and Artificial Intelligence services, the collaboration aimed to give safe, open-source Python and R tools and packages.. Key drivers for this market are: Growing adoption of Cloud Based Solutions, Solutions Segment is Expected to Hold a Larger Market Size. Potential restraints include: Lack of Standardization and Security Concerns. Notable trends are: Solutions Segment is Expected to Hold a Larger Market Size.

The Data Catalog Market, valued at $2.61 billion in 2025, is projected to experience steady growth, driven by the escalating need for data governance, improved data quality, and the rising adoption of cloud-based data solutions. The Compound Annual Growth Rate (CAGR) of 2.50% over the forecast period (2025-2033) indicates a consistent, albeit moderate, expansion. This growth is fueled by several key factors. Organizations are increasingly recognizing the strategic value of their data assets and are investing heavily in tools and technologies that enhance data discoverability, accessibility, and usability. The increasing complexity of data landscapes, with data residing across diverse sources and formats, further necessitates the implementation of robust data cataloging solutions. The market's growth is also being propelled by the growing adoption of big data analytics, machine learning, and artificial intelligence, all of which rely heavily on the efficient management and organization of data. Furthermore, stringent data privacy regulations such as GDPR and CCPA are driving demand for solutions that ensure data compliance and traceability. Leading players like IBM, Microsoft, and Informatica are actively shaping the market landscape through continuous innovation, strategic partnerships, and acquisitions. While the market enjoys consistent growth, challenges remain. The high initial investment costs associated with implementing and maintaining data cataloging solutions can pose a barrier for smaller organizations. Furthermore, ensuring data quality and consistency across diverse data sources remains a significant hurdle. Despite these challenges, the long-term outlook for the data catalog market remains positive, driven by the ongoing digital transformation initiatives undertaken by businesses worldwide and the growing realization of the strategic imperative to effectively manage and leverage data assets. The market is expected to reach approximately $3.3 billion by 2033. Recent developments include: November 2022 - Amazon EMR customers can now use AWS Glue Data Catalog from their streaming and batch SQL workflows on Flink. The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog. With this release, Companies can directly run Flink SQL queries against the tables stored in the Data Catalog., September 2022 - Syniti, a global leader in enterprise data management, updated new data quality and catalog capabilities available in its industry-leading Syniti Knowledge Platform, building on the enhancements in data migration and data matching added earlier this year. The Syniti Knowledge Platform now includes data quality, catalog, matching, replication, migration, and governance, all available under one login in a single cloud solution. It provides users with a complete and unified data management platform enabling them to deliver faster and better business outcomes with data they can trust., August 2022 - Oracle Cloud Infrastructure collaborated with Anaconda, the world's most recognized data science platform provider. By permitting and integrating the latter company's repository throughout OCI Machine Learning and Artificial Intelligence services, the collaboration aimed to give safe, open-source Python and R tools and packages.. Key drivers for this market are: Growing adoption of Cloud Based Solutions, Solutions Segment is Expected to Hold a Larger Market Size. Potential restraints include: Growing adoption of Cloud Based Solutions, Solutions Segment is Expected to Hold a Larger Market Size. Notable trends are: Solutions Segment is Expected to Hold a Larger Market Size.

Televisions

This dataset was extracted, transformed and loaded using various sources. The entire ETL Process looks as follows:

https://github.com/IyadElwy/Televisions/assets/83036619/7088d477-2559-4af2-94e9-924274521d36" alt="data_pipeline">

Links

Github ETL Process Code

Kaggle Dataset

Explanation

• First I needed to find some appropriate data-sources. For this I used Wikiquote to extract important and unique script-text from the various shows. Wikipedia was used for the extraction of generic info like summaries, etc. Metacritic was used for the extraction of user reviews (scores + opinions) and the Opensource TvMaze API was used for getting all sorts of data, ranging from titles to cast, episodes, summaries and more.
• Now the first step was to gather titles from those sources. It was important to divide the scrapers into two categories, scrapers that get the titles and scrapers that do the heavy-scraping which gets you the actual data.
• For the ETL job orchastration Apache Airflow was used which was hosted on an Azure VM instance with 4 Virtual CPUs and about 14 GBs of RAM. This was needed because of the heavy Sprak data transformations
• RDS, running PostgreSQL was used to save the titles and their corresponding urls
• S3 buckets were mostly used as Data Lakes to hold either raw data or temp data that needed to be further processed
• AWS Glue was used to do Transformations on the dataset and then output to Redshift which was our Data Warehouse in this case

CosmosDB NoSQL Schema

It's important to note the additionalProperties field which makes the addition of more data to the field possible. I.e. the following fields will have alot more nested data. json { "type": "object", "additionalProperties": true, "properties": { "id": { "type": "integer" }, "title": { "type": "string" }, "normalized_title": { "type": "string" }, "wikipedia_url": { "type": "string" }, "wikiquotes_url": { "type": "string" }, "eztv_url": { "type": "string" }, "metacritic_url": { "type": "string" }, "wikipedia": { "type": "object", "additionalProperties": true }, "wikiquotes": { "type": "object", "additionalProperties": true }, "metacritic": { "type": "object", "additionalProperties": true }, "tvMaze": { "type": "object", "additionalProperties": true } } }