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.

The Data Catalog Marketsize was valued at USD 878.8 USD million in 2023 and is projected to reach USD 2749.95 USD million by 2032, exhibiting a CAGR of 17.7 % during the forecast period. Data catalog is another concept that is used to refer to a unified list of all the data resources within an organization and their descriptions that are crucial in the course of data search. It can also sort data, effectively making it easier to find and use data sets that the user requires. based on their usage, data catalogs can be distinguished into business, technical, and operation catalogs; business use for business intelligence, technical for providing metadata for technical use, and operational use for tracking operational data. Some of the significant elements of data catalogs are data lineage, metadata management, search and discovery features, data governance, and collaboration. They are actively utilized in industries for increasing data quality, satisfying the requirements of compliance, and optimizing the analysis to support better decision-making and increase efficiency in business operations. Recent developments include: February 2024 – Collibra launched Collibra AI Governance, built on their Data Intelligence Platform, enabling organizations to deliver trusted AI effectively through the use of Collibra Data Catalog. It aided teams in collaborating for compliance, improved model performance, reduced risk, and led to faster production timelines., September 2023 – AWS Lake Formation launched a Hybrid Access Module for the AWS Glue Data Catalog, allowing users to selectively enable Lake Formation for tables and databases without interrupting existing users or workloads. This feature provided flexibility and an integral path for enabling Lake Formation, reducing the need for coordination among owners and consumers., July 2023 – Teradata acquired Stemma Technologies to enhance its analytics capabilities, particularly in data discovery and delivery. Stemma’s automated data catalog bolstered Teradata’s offerings, aiming to improve user experience and accelerate ML and AI analytics growth., June 2023 – Acryl Data secured USD 21 million in Series A funding led by 8VC to enhance its open-source data catalog platform. This investment enhanced their cloud offerings and expanded their vision towards a data control plane., May 2023 – data.world launched its new Data Catalog Platform, integrating generative AI bots to enhance data discovery. With over 2 million users, the platform aimed to make data discovery and knowledge unlocking accessible to users of all expertise levels., February 2023 – data.world, a data governance platform, launched the first AI Lab for the data catalog industry. This Artificial Intelligence (AI) Lab would be important in bringing partners and customers together to enhance data team productivity using AI technology., November 2022 – Amazon Web Services (AWS) launched DataZone, a new machine learning-based data management service to help enterprises catalog, share, govern, and discover their data quickly.. Key drivers for this market are: Exponential Growth of Data Volume and Data Analytics to Fuel Market Growth. Potential restraints include: High Initial Deployment Cost and Privacy Concerns to Hinder Market Growth. Notable trends are: Growing Adoption of AI and Automation Technologies to Amplify Market Growth.

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 } } }

The Data Pipeline Tools Market size was valued at USD 8.4 billion in 2023 and is projected to reach USD 38.95 billion by 2032, exhibiting a CAGR of 24.5 % during the forecasts period. Data pipeline tools are software solutions engineered to streamline and automate the efficient movement of data from diverse sources to destinations like databases, data warehouses, or analytical systems. These tools are pivotal in contemporary data architecture, facilitating the ingestion, processing, transformation, and storage of data. They typically offer functionalities such as extracting data from sources (e.g., databases, APIs, files), transforming data (cleaning, filtering, aggregating), and loading data into target systems. Key characteristics of data pipeline tools include scalability to manage large data volumes, fault tolerance to ensure data reliability and integrity, and support for both real-time and batch processing based on business requirements. They often provide graphical user interfaces or APIs for configuring data workflows, scheduling tasks, monitoring data flows, and managing dependencies between operations. Data pipeline tools cater to a wide range of applications across industries, encompassing data integration for business intelligence, system-to-system data migration, ETL processes for data warehousing, and real-time data processing for operational analytics. Notable examples of these tools include Apache Airflow, Apache Kafka, AWS Glue, Google Cloud Dataflow, and Informatica. By automating data workflows and maintaining consistency and reliability in data movement, these tools empower organizations to accelerate decision-making, enhance data quality, and optimize operational efficiency. They are indispensable for modern enterprises striving to harness data as a strategic asset for achieving competitive advantages and fostering business growth.