The proliferation of time-series big data has presented unprecedented challenges and opportunities in various domains, including finance, healthcare, environmental monitoring, and industrial processes. Effective and efficient query processing for time-series data is crucial for extracting valuable insights and making informed decisions. This paper provides an overview of techniques and tools for temporal query processing in the context of time-series big data. We first discuss the unique characteristics of time-series data, such as high dimensionality, temporal dependencies, and varying data rates, which necessitate specialized approaches for querying and analyzing this data. We then delve into the key techniques employed for temporal query processing, including data indexing, compression, and similarity measures. These techniques enable the storage and retrieval of time-series data efficiently, making it possible to execute complex queries in real time. Furthermore, we discuss the importance of distributed computing and parallel processing in handling the massive volumes of time-series data generated daily. Scalability and fault tolerance are critical factors in designing systems that can handle ever-increasing data loads efficiently. We explore how various distributed processing frameworks can be harnessed to meet these requirements.