# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 3 VBDA Architectures and Core Technologies
### VBDA Core Technologies
#### 3.2.4 Storage and Indexing in VBDA Systems  
Efficient storage and indexing are critical to managing the scale, speed, and variety of video data in VBDA systems. Contemporary solutions favor hybrid architectures that blend data lakes with data warehouses, offering both flexibility and structured querying. In parallel, AI-driven indexing techniques are increasingly used to automate metadata generation, support compression, and enable scalable organization of video content. As shown in Table 6, these advances are designed to provide low-latency access, reduce storage costs, and support the dynamic requirements of large-scale video analytics. Data lakes serve as the backbone of scalable video storage in VBDA systems, offering the flexibility to ingest diverse and unstructured content, including video streams, sensor logs, and metadata. Hadoop HDFS has played a central role due to its robustness and batch-processing capabilities, cited in many of the systems surveyed [11]. However, its high-latency access and rigid structure have led to a gradual transition toward cloud-native object storage platforms such as Amazon S3, Google Cloud Storage, and Azure Data Lake. These services provide elastic scalability and seamless integration with big data pipelines and AI workloads [7]. In more localized or hybrid settings, lightweight solutions such as MinIO present S3-compatible APIs with lower overhead, making them well suited for edge-centric deployments [11]. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**FL Algorithms**} & \multicolumn{2}{c}{**FL Frameworks**} & \multicolumn{2}{c}{**Privacy**} & \multicolumn{2}{c}{**Model Optimization**} & \multicolumn{2}{c}{**Edge AI Hardware**} & \multirow{2}{*}{**Edge-Cloud Orchestration**} \\ \cline{2-2} \cline{11}  & **FedAvg** & **FedProx** & **FedM1** & **TTF** & **Techs** & **Prun.** & **Quant.** & **KD** & **Jetson** & **Coral** & **Moviations** \\ \[PERSON] et al. [137] & 2025 & ✓ & ✓ & & & & ✓ & & & & & & \\ [PERSON] et al. [139] & 2024 & ✓ & & & & ✓ & & & & & & & \\ [PERSON] et al. [140] & 2024 & & & & & & & & ✓ & ✓ & & & \\ [PERSON] et al. [8] & 2024 & ✓ & & & & & & & & & & ✓ & \\ [PERSON] et al. [3] & 2024 & ✓ & & & ✓ & ✓ & ✓ & ✓ & ✓ & & & ✓ & \\ [PERSON] et al. [50] & 2024 & ✓ & & & & ✓ & & & & & & & ✓ \\ [PERSON] et al. [19] & 2023 & ✓ & & & & ✓ & & & & & & & ✓ \\ [PERSON] et al. [5] & 2023 & ✓ & & & & ✓ & & & & ✓ & ✓ & ✓ & ✓ \\ [PERSON] et al. [138] & 2023 & ✓ & ✓ & ✓ & ✓ & & & & & & & ✓ \\ [PERSON] et al. [14] & 2022 & ✓ & & & & ✓ & ✓ & ✓ & & & & & & ✓ \\ [PERSON] et al. [13] & 2022 & & & & & & & & ✓ & ✓ & & & ✓ \\ [PERSON] et al. [4] & 2022 & & & & & & & ✓ & ✓ & ✓ & & & ✓ \\ [PERSON] et al. [141] & 2020 & ✓ & ✓ & ✓ & ✓ & & & & & & & \\ \hline
**Total** & & **10** & **3** & **3** & **4** & **6** & **2** & **1** & **3** & **5** & **2** & **3** & **9** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Federated learning and edge AI for VBDA. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Gload Straggs**} & \multicolumn{4}{c}{**NoSQL Dms**} & \multicolumn{4}{c}{**SQL Dms**} \\ \cline{3-14}
**Reference** & **Year** & **Maddrop (MIPS)** & **Ansum** & **Gengle** & **Ansum Data** & **HBase** & **Mango DB** & **Causenda** & **Gengle** & **MgSQL**  
For managing dynamic and schema-flexible metadata, NoSQL databases such as MongoDB, Cassandra, and HBase are commonly used to support distributed ingestion and real-time querying. In parallel, graph databases like Neo4j and TigerGraph are increasingly adopted for modeling semantic relationships between objects, scenes, and events. These models enable sophisticated reasoning and contextual analytics, especially in surveillance and smart city scenarios [145]. Traditional data warehouses continue to play a vital role in structured analytics and metadata management.