At Gemini, we provide our users with tools to explore the crypto world, whether they want to trade, store, and stake crypto, or simply learn more about our expansive industry. Today, we are thrilled to unveil __the BitcoinBuzz Indicator, a tool to empower crypto enthusiasts and traders with dynamic insights into the world of Bitcoin and crypto.__ 

__Every Friday, the BitcoinBuzz Indicator* will be included in our Weekly Market Updates.__ In each Weekly Market Update, we will display an updated BitcoinBuzz dial and its components (more on them below), showing how each has changed over the past week. 

<a href="#What is the BitcoinBuzz Indicator?" target="">__What is the BitcoinBuzz Indicator?__</a> 
<a href="#Understanding the Buzz" target="">__Understanding the Buzz__</a> 
<a href="#BitcoinBuzz Indicator Components" target="">__BitcoinBuzz Indicator Components__</a> 
<a href="#Analyzing and Combining the Components" target="">__Analyzing and Combining the Components__</a> 
<a href="#How Has BitcoinBuzz Fared Historically?" target="">__How Has BitcoinBuzz Fared Historically?__</a>

![0613 Dial](//images.ctfassets.net/jg6lo9a2ukvr/cfH7AZeR8G8j4TSp03Bfz/ba4945a6e2d7a7f1bb1e7fc2b61fe3c3/0613_Dial.png)



### What is the BitcoinBuzz Indicator?

BitcoinBuzz is a cutting-edge BTC indicator that offers a comprehensive view of price movements. By harnessing in-depth sentiment analysis, BitcoinBuzz combines news sentiment, on-chain BTC flows, Gemini BTC volumes, BTCUSD price momentum, and BTCUSD breadth. 

Together, these components paint a picture of BTC sentiment swings, with a user-friendly score between 1 and 10. While certainly not forward looking, the BitcoinBuzz Indicator offers a clear and concise snapshot of sentiment trends, helping you better understand the rapidly evolving BTC market. 



### Understanding the Buzz

We’ve divided the BitcoinBuzz scale into 5 categories, ranging from Buzzing Frenzy, reflecting the relative highest potential buzz around Bitcoin, to Buzzing Lull, implying a quiet period for Bitcoin.

__BitcoinBuzz Scale__

1. __Buzzing Frenzy: (8.5-10):__ There is an extremely high level of buzz and excitement surrounding Bitcoin. This represents a state of intense market activity, widespread interest, and a surge of positive sentiment. It suggests a market in a state of euphoria.
2. __Buzzing Chatter (6-8.5):__ There is a high level of discussion, attention, and positive sentiment surrounding Bitcoin, suggesting an active and engaged market, with increased awareness and interest among investors. This indicates a market that is abuzz with positive discussions and anticipation.
3. __Buzzing Shift (4-6):__ This reflects a transitional phase in market sentiment and buzz surrounding Bitcoin. It suggests a shift or change in the prevailing sentiment, indicating a potential turning point in market dynamics. This category acknowledges the fluid nature of market sentiment and the possibility of a shift in market conditions.
4. __Buzzing Whispers (2.5-4):__ This represents a relatively quiet and subdued market environment and discussion around Bitcoin. It suggests a lack of significant attention or enthusiasm, potentially indicating a period of consolidation or decreased interest from market participants.
5. __Buzzing Lull (1-2.5):__ This denotes an extremely low level of buzz and interest surrounding Bitcoin. It represents a state of minimal activity, diminished discussion, and negative sentiment. It may indicate a market slump, lack of confidence, or a significant decline in interest and engagement.



### BitcoinBuzz Indicator Components

The Indicator score, between 1-10, incorporates various data points, including:

- Crypto news sentiment 
- Onchain BTC flows 
- Gemini BTC volumes 
- BTCUSD price momentum 
- BTCUSD breadth

At its core, BitcoinBuzz is a composite indicator that analyzes the variables above to arrive at a single score. A reading above 7 or below 3 suggests extreme market conditions, with higher numbers indicating positive sentiment and lower numbers indicating negative sentiment, as outlined above.



### Analyzing and Combining the BitcoinBuzz Components

To distill the content data we collect, we use advanced techniques to extract sentiment from crypto news and combine that with BTC price and on-chain data. 

__Crypto News__

Our *crypto news* sentiment indicator uses word embeddings to determine whether news on BTC is positive or negative. Word embeddings are numerical representations of words that capture their semantic relationships, enabling machines to understand and process language more effectively. 

__BTC Price and Flows__ 

*Onchain BTC flows* provide insights into the activities of BTC whale wallets that interact with the Gemini Exchange. *Gemini BTC volumes* represent an overall view of Gemini Exchange volumes that can reflect overall buying and selling pressure in the market.

*BTCUSD momentum* helps identify BTC price movements by considering various moving average pairs and short- and long-term volatility. This element combines these moving averages and volatility parameters to arrive at a single risk-adjusted reading of BTCUSD momentum. 

*BTCUSD breadth* measures the strength of BTC's price relative to its 52-week high and low, as well as its distance from the upper and lower [Bollinger Bands](https://www.gemini.com/cryptopedia/glossary#bollinger-bands). By tracking the relative strength of BTC, a general trend develops to enhance the descriptive nature of the BitcoinBuzz Indicator.

__Putting it All Together__

These components are subsequently smoothed out using a 30-day exponential moving average to reduce noise and whipsaws. The ultimate BitcoinBuzz Indicator between 1 and 10 is defined by the sum of mean-weighted results of all these components, and is further smoothed using a five-day exponential moving average to account for volatility differences between the components. 



### How Has BitcoinBuzz Fared Historically?

The chart below shows the price of bitcoin in orange overlaid on the BitcoinBuzz Indicator score.

![0613 Chart](//images.ctfassets.net/jg6lo9a2ukvr/7koCZja1Zfg0gTeLgENs2n/6899b85fcd0600906bf4aafb36f0cb14/0613_Chart.png)

Keep an eye out for the BitcoinBuzz Indicator in our Weekly Market Updates, to help you gauge the overall sentiment and momentum of the BTC market over the past week.

Onward and Upward!

Gemini Analytics Team

*This material is for informational purposes only and is not (i) an offer, or solicitation of an offer, to invest in, or to buy or sell, any interests or shares, or to participate in any investment or trading strategy, (ii) intended to provide accounting, legal, or tax advice, or investment recommendations, or (iii) an official statement of Gemini. Gemini, its affiliates and its employees do not make any representation or warranty, expressed or implied, as to accuracy or completeness of the information or any other information transmitted or made available. Buying, selling, and trading cryptocurrency involves risks, including the risk of losing all of the invested amount. Recipients should consult their advisors before making any investment decision. Any use, review, retransmission, distribution, or reproduction of these materials, in whole or in part, is strictly prohibited in any form without the express written approval of Gemini.

Introducing Gemini's BitcoinBuzz Sentiment Indicator

- Our Analytics team built a modern data platform using Databricks Lakehouse to automate and simplify data processes, providing a foundation for future data innovations.
- The Gemini Dynamic Data Load (GDDL) architecture was built to manage a growing data landscape and provide self-service capabilities by leveraging the powerful features of Databricks and AWS.
- This architecture ensures automatic scaling, structural evolution, and efficient replication and loading of data into our Lakehouse within defined SLAs.

![Blog Inline 03](//images.ctfassets.net/jg6lo9a2ukvr/6NjikZ3DzdLHZdqBx8G1oG/87208c48dc03f7b455a92a1085b1de34/Blog_Inline_03.png)

The Gemini Analytics team strives to automate, simplify, and build processes that provide a strong foundation for future data innovations. Contained within the Analytics team is the Data Engineering team, which is specifically tasked with managing the ever-expanding data landscape to deliver advanced data capabilities, while minimizing costs. This has led to a focus on simplification and automation, which is a continuous process that evolves over time and adapts to technical and business needs.

## Problem Overview 

As data becomes increasingly vital to organizations, data engineers, machine learning engineers, and analytics engineers all rely on the work of data engineering and platform teams to deliver high-quality data. These teams face a range of tasks, including:

- Ingesting new data into the system at various intervals, including streaming data;
- Validating and formatting data to meet the needs of different sizes and time periods;
- Updating and managing data structures, regardless of whether the data and platform are centralized or distributed;
- Minimizing infrastructure costs while maintaining performance and reliability.

As the number of data-related requests continues to rise, managing the resulting growth in data requires a constant influx of resources, time, and effort. The sheer scale of this data behemoth demands a rigorous approach to ensure that data is accurate, accessible, and secure.

Business requirements and engineering challenges have driven us to develop a robust solution that addresses these needs. __Our Gemini Dynamic Data Load (GDDL) architecture is designed to auto-scale, structurally evolve, unify data, and provide self-service capabilities.__ This is achieved by leveraging the powerful features of [Databricks](https://www.databricks.com/) and Amazon Web Services (AWS).

## Solution Overview

In the Gemini data universe, data is sourced from a variety of systems, including Online Transaction Processing (OLTP) systems, message buses, No-SQL databases, API ingestions,  and external third party data captures. __This blog post focuses specifically on our GDDL solution which was developed to replicate and ingest data from the core OLTP systems housing thousands of tables.__ 

To understand our solution, it's important to first understand our overall data processes. From a data architecture perspective, we replicate historical and incremental Change Data Capture (CDC) data from different source systems to our Amazon Simple Storage Service (S3) bucket. This data is then unified in the GDDL architecture to meet critical business needs and enable downstream reporting.

__The GDDL architecture__

The GDDL architecture serves a range of functions that work together and enable us to parse and deliver relevant data quickly and accurately. These functions include:

1. Capturing changes made to data in a database and delivering them to a file system (S3) in real time or near real time;
2. Loading incremental CDC data into one table and replicating transactional database data into a separate table in chronological order based on its nature and size, using either a pre-defined schedule or a custom schedule;
3. Leveraging optimal compute that ingests tables based on their sizes (small, medium, and large tables);
4. Onboarding new datasets or tables dynamically with minimal ad-hoc development;
5. Structurally evolving models for data migrations (e.g. the addition or removal of columns at the source database);
6. Establishing service-level agreements (SLAs) for specific tables by optimizing compute-level performance and providing flexibility for ad-hoc requests.

![Blog Data Platform 01](//images.ctfassets.net/jg6lo9a2ukvr/2z3kU1RI9LpVwZMFo8Y09I/e563076c1866a67e04737dcd21ef8b7b/Blog_Data_Platform_01.png)
Figure 1: Gemini’s Cloud Architecture for Data Ingestion. This diagram illustrates the high-level solution architecture. 

Step 1: Database Migration Service (AWS DMS) streams data from Relational Database Service (Amazon RDS) and Aurora (Amazon Aurora) hosted in different AWS accounts.  
Step 2: DMS delivers streaming data to Amazon S3.  
Step 3: Amazon Managed Workflows for Apache Airflow (Amazon MWAA) tasks run in specific schedules and check for the availability of data on Amazon S3.  
Step 4(a): Scheduled tasks in Amazon MWAA invoke Databricks Workflows.  
Step 4(b): Databricks Jobs as part of Databricks Workflow loads data into the Lakehouse on Amazon S3.  
Step 5: Amazon MWAA tasks invoke internal ETL jobs for downstreams.  
Step 6: Downstream ETL jobs consume data from Lakehouse on Amazon S3.

## Architecture Guidelines

Below are the guiding principles we followed during the development of GDDL:

1. __Unified Modular Code:__ Modularization and parameterization were used to arrive at a unified code that can easily be enhanced and extended.
2. __Auto-Concurrent Data Loads:__ Ingest thousands of tables concurrently with zero manual intervention or auto-throttled invocations.
3. __Job Maintainability/Dependency Management:__ An orchestration mechanism and dependency management were built into the architecture to facilitate easier maintenance and re-run of jobs by on-call engineers and subject matter experts (SMEs).
4. __Streaming vs. Batch Fashion:__ The code architecture is easily adaptable to real-time or near-real-time streaming needs, with simple configuration-driven code changes.
5. __Service Driven:__ Jobs were exposed as a service through a service-oriented architecture, enabling internal non-data teams and engineers, such as Marketing, to set dependencies on data jobs.

## Core Architecture and Implementation

![Blog Data Platform 02](//images.ctfassets.net/jg6lo9a2ukvr/5zY488rE4XzGebo7FpoEP0/8951d82d9b9fdd1dc4b25c890562ed67/Blog_Data_Platform_02.png)
Figure 2: GDDL Architecture has three components detailed in the sections below.

__1. Source__
- Data is ingested to S3 in Parquet format using third-party data-ingestion tools or cloud-native services like [AWS Database Migration Service (DMS)](https://aws.amazon.com/dms/).
- Custom ingestion tools and the database migration service load one-time initial data and stream incremental CDC data on a specific frequency from the replication logs of transactional databases with different schema types.
- Predictive analytics and Business Intelligence (BI) reporting require CDC data and full transactional data to be preserved in separate delta tables. These requirements in turn demand schema synthesis and data unification so that the destination delta table reflects the true replica of the source for downstream consumption.

__2. Processing Service__
- Initial and incremental CDC data are unified into raw delta tables using Structured Spark streaming by leveraging Auto Loader capabilities to meet batch and streaming needs.
- The processing service layer aims to unify data for user consumption and downstream processing.
- Our Spark jobs are designed to be parameterized using table name, schema name, and a flag indicating whether a full refresh is required. When an incoming Airflow API request is received, PySpark code reads these parameters and constructs the S3 directory location. This information is then passed to the Auto Loader component of the PySpark code, which processes the existing or new files as they arrive into the S3 directory.
- Our processing service replicates the raw data to delta tables that correspond to the bronze layer of the [medallion architecture](https://www.databricks.com/glossary/medallion-architecture). This ensures that the data is transformed and stored in a way that facilitates downstream analysis and reporting.

__3. Orchestration Service__
- Our solution generates dynamic Airflow Directed Acyclic Graphs (DAGs) and tasks using Python code on MWAA, which leverages metadata managed in S3, including table name, primary key, and table size. This approach enables us to dynamically add tables, datasets, and job invocations in an elastic manner, by invoking corresponding Databricks Workflows.

As we outline above, DMS acts as the cardinal replication service in our architecture. We will now hone in on how we built out our processing and orchestration service.

There are two stages involved in the processing service to arrive at the final table:

- __Stage1 → Initial Load/Main Table:__ Our Auto Loader code merges newly arrived data into the final raw delta table using its primary key. If this is the first time the data is being loaded, the code creates the main table and writes the files to the final raw delta table location. This approach ensures that the data is accurately and efficiently loaded into the system.
- __Stage2 → Incremental Load/Incremental Table:__ Our Auto Loader appends the incoming new CDC data to the incremental delta table location and merges it with the final raw delta table.

![Blog Data Platform 03](//images.ctfassets.net/jg6lo9a2ukvr/3Zn5gsy7CZVHGh1XdJwvCm/b21080751b49ead834e2beb1b3b02b94/Blog_Data_Platform_03.png)
Figure 3: Leveraging Databricks Auto Loader to simplify ingestion of change data.

We noticed that the streaming data can have multiple incremental CDC events for the same primary key for a specific time frame, since multiple events could happen for the same primary key. In order to execute a successful merge, the data team used a window function at the micro batch level to sort the records using the date column in the metadata delta table, pick up the latest row, and perform a merge. Please refer to the Python pseudo code below that takes care of schema evolution and data merges. Also refer to [Introducing Window Functions in Spark SQL](https://databricks.com/blog/2015/07/15/introducing-window-functions-in-spark-sql.html). 

    def upsert_query_constructor(self):
    # Constructs string that is pre populated for on clause.
    on_clause_string=' and '.join(on_clause_constructor(self))
    merge_query = f"""
      MERGE INTO dms_{self.dataset}.{self.table} AS t 
        USING updates AS s 
        ON {on_clause_string}
        WHEN MATCHED AND s.transact_seq > t.transact_seq 
          AND Op in ('U', 'I') THEN UPDATE SET * 
        WHEN MATCHED AND s.transact_seq > t.transact_seq 
          AND Op = 'D' THEN DELETE 
        WHEN NOT MATCHED AND Op in ('U', 'I') THEN INSERT *
    """
    return merge_query

    def upsertToDeltaDF(microBatchOutputDF, batchId):
    windowSpec = (
        Window
        .partitionBy(self.primary_keys)
        .orderBy(col('transact_seq')
        .desc()
    )
    dframe_with_rank = (
        microBatchOutputDF
        .withColumn("dense_rank", dense_rank().over(windowSpec))
    )
    microBatchOutputDframe_rank = (
        dframe_with_rank
        .filter(dframe_with_rank.dense_rank == 1)
    )
    microBatchOutputDframe_rank.createOrReplaceTempView("updates")
    microBatchOutputDframe_rank._jdf.sparkSession().sql(upsert_query_constructor(self))

    # Parameter to enable Schema Evolution.
    spark.sql("SET spark.databricks.delta.schema.autoMerge.enabled = true")

    staging_table_1_data
    .writeStream
    .format("delta")
    .option("mergeSchema", "true")  # Option to enable new schema merges.
    .option("checkpointLocation", target_checkpoint)
    .foreachBatch(upsertToDeltaDF) 
    .trigger(once=True)  # Enable continuous streaming if required.
    .outputMode("update") 
    .start()

We can enhance the existing batch jobs to support near-real-time data streaming by changing the trigger parameter of readstream from (Trigger.Once) to (Trigger.Continuous).

Some of the key approaches to the existing architecture include: 

- Primary keys for each table are populated and maintained in a separate S3 config file through a separate semi-automated process. 
- Automatically create tables if they do not exist using the PySpark code (during Stage1 and Stage2 of the processing service).
- Leveraging Auto Loaders at each stage of our pipeline to ensure compatibility with both streaming and batch processing requirements. This approach enables us to seamlessly process data in either mode, depending on the specific needs of the business.
- Ability to perform full refresh of an existing table using an input argument.

Up to now, we have discussed the processing layer of our data architecture. We will now move on to the orchestration layer. The orchestration layer is a critical  component that manages the interactions between a variety of data engineering processes, including Amazon MWAA (Airflow DAGS), [Databricks Workflows](https://www.databricks.com/blog/2022/05/10/introducing-databricks-workflows.html), and  parameterized Databricks Jobs. 

To facilitate this, we use Amazon MWAA as the main orchestration framework for authoring, scheduling and monitoring workflows for a variety of Exchange, Transfer, Load (ETL) processes, and Databricks Workflows to orchestrate services specific to Lakehouse. These services include Databricks Jobs, job run monitoring, and job concurrency management. By leveraging these tools, we are able to efficiently manage and streamline our data engineering processes.

In designing the GDDL architecture, we implemented automatic scaling of Amazon MWAA based on demand for dynamic DAG creation, using the metadata definition file stored in S3. This metadata includes information such as the size, priority, and primary keys of the tables, which are used to invoke specific Databricks workflows to optimize compute costs and meet SLAs. 

When needing to onboard new tables, teams like Machine Learning can self-serve by adding the metadata definitions themselves. In order to meet the platform needs of new table additions, concurrent DAG and task runs are scaled out by periodically adjusting parameters related to scaling in Airflow, such as DAG_CONCURRENCY and PARALLELISM. This approach enables us to efficiently manage resources and meet performance requirements in a cost-effective manner.

![Blog Data Platform 04](//images.ctfassets.net/jg6lo9a2ukvr/6tlhtlFleBnKCSlTVsaROL/7be22e11849ffc810dfd8f2d4b3be044/Blog_Data_Platform_04.png)
Figure 4: Incremental ingestion of data using Databricks Workflows into delta tables.

Data is loaded chronologically to the Lakehouse by executing the initial and incremental tasks of Databricks workflows. Job concurrency limits are set to 1,000 in order to perform large-scale parallel production data loads. Built-in monitoring capabilities of Databricks Workflows, especially the matrix view, is being used extensively by the team to constantly observe job run times and act upon them when appropriate. 

![Blog Data Platform 05](//images.ctfassets.net/jg6lo9a2ukvr/2tshGX45TKDGO2Ika7YAR2/a9b8ef702db291c91988ce971052a60b/Blog_Data_Platform_05.png)
Figure 5: Visual of job monitoring matrix in Databricks.

## Future Considerations

As we continue to iteratively improve our core capabilities and ensure the GDDL framework meet the highest standards, we have identified several enhancements that we plan to implement:

- Given the success of this overall framework, the team will be adapting and extending the GDDL architecture to other source systems, such as NoSQL databases and messaging systems. 
- Leverage [Delta Live Tables (DLT)](https://www.databricks.com/product/delta-live-tables) for our Databricks Workflows to take advantage of its built-in data quality controls and operational efficiencies.
- Integrate workflow metrics with observability solutions like Datadog for proactive monitoring. 

## Conclusion

To meet the technical challenges and business requirements of our expanding data universe, Gemini developed the GDDL architecture using powerful features and services of AWS and Databricks such as MWAA, DMS, [Databricks Autoloader](https://docs.databricks.com/ingestion/auto-loader/index.html) and Databricks Workflows. This architecture ensures automatic scaling, structural evolution, and efficient replication and loading of data into our Lakehouse within defined SLAs. 

As a result, our team benefits from a highly efficient data replication process that is scalable, supports schema evolution, and is both config-driven and automated. This approach allows us to set up data ingestion for new databases within minutes, saving significant time and resources that would otherwise be required to develop data pipelines manually from scratch. By using a configuration-driven approach to choose batch run times, we have also been able to significantly reduce compute costs involved in our ETL processes. 

Given the success of the GDDL architecture, we have adapted it across the Gemini organization for seamless data replication and ingestion, and we are looking forward to continuing to iterate and improve on our processes. 

Gemini Analytics Team

![Blog Inline 03](//images.ctfassets.net/jg6lo9a2ukvr/6NjikZ3DzdLHZdqBx8G1oG/87208c48dc03f7b455a92a1085b1de34/Blog_Inline_03.png)
About the authors:

__Keerthivasan Santhanakrishnan__ is a Senior Data Engineer at Gemini with a focus on the architecture, design, and implementation of real-time streaming, data ingestion, and data platform solutions using big data and cloud computing technologies. 

__Anil Kumar Kovvuri__ is a Principal Data Engineer at Gemini. He is responsible for leading the overall Data Lake architecture and currently oversees blockchain analytics at Gemini.

__Sriram Rajappa__ is the GM for Gemini Analytics. Apart from being in charge of running the data program at Gemini, Sriram mentors, encourages, and guides the team on overall big data and analytics architecture. 

*Decentralization and transparency are central to crypto's ethos. At Gemini, we want to contribute to greater understanding and knowledge around fast-paced technical developments in crypto and help further drive innovation in our industry. In this blog, we are proud to provide a glimpse of a data engineering architecture developed by our engineers as we continue to build the future of crypto.*

Gemini Analytics Team

ARTIGOS DE Gemini Analytics Team

Introducing Gemini's BitcoinBuzz Sentiment Indicator

How We Built a Modern Data Platform at Gemini

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