From one of the leading contributors of Apache Arrow DataFusion,
a next generation data infrastructure enabling AI-native data applications.
From the leading contributors of Apache Arrow DataFusion, a next generation data infrastructure enabling AI-Native Data Applications.
Get Early Access!.png)
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SELECT
NOW() AS ts,
GENERATE('GPT-4.0',
messages.text,
f'{
"system content": { LAST(prompts.prompt ORDER BY prompts.ts) }
}’)::json
) -> 'choices' -> 'message' ->> 'content' AS response
INTO responses
FROM messages, prompts
WHERE messages.ts >= prompts.ts
GROUP BY messages.ts
From one of the leading contributors of Apache Arrow DataFusion, a next generation data infrastructure enabling AI-native data applications.
From the leading contributors of Apache Arrow DataFusion, a next generation data infrastructure enabling AI-Native Data Applications.
Get Early Access!
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Apache Arrow DataFusion offers a potent unified data processing solution, crafted in Rust. Leveraging the Apache Arrow in-memory format, it delivers rapid query performance and supports both SQL and Dataframe APIs. With innate compatibility for CSV, Parquet, JSON, and Avro, it boasts extensive adaptability and thrives on a supportive community -> Learn more
Synnada seamlessly integrates with the your data stack, including warehouses, lakehouses, orchestration, and metrics layer.
The traditional route to building data intensive applications is a path of exhaustion, beset with millions of questions and hurdles.
Let's change the narrative!
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Build real-time data pipelines within minutes using standard SQL and convert them to live end-to-end applications.
Seamlessly work with at-rest and in-motion data sets with Synnada's stream-first data processing technology. Implementing the Kappa architecture through Apache Datafusion’s innovative approach, we ensure efficient handling of large-scale data workloads in real-time.
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Craft tailor-made solutions through your own applications by composing query blocks and a rich selection of utility blocks. The notebook interface allows you to easily assemble and test these building blocks, delivering real-time data applications that drive impactful actions.
SELECT
NOW() AS ts,
GENERATE(‘GPT-4.0’, cb_prompts.text,
‘{
“system_content”: prompts_source.prompt
}’::json
) -> ‘choices’ -> ‘message’ ->> ‘content’ AS response
INTO cb_response
FROM cb_prompts, prompts_source
ORDER BY ts;
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Seamlessly incorporate agile methodologies into your data engineering workflows. Utilize intelligent change management to visualize the impact of updates on your data applications in real-time, ensuring that your pipelines evolve without disruption.
SELECT
cs.*
(COUNT(CASE WHEN call_status="drop" THEN 1 END)
OVER time_range) AS drops
(COUNT(CASE WHEN call_status="drop" THEN 1 END) * 100.0 / COUNT(*) OVER time_range) AS drop_rate
FROM aws_na37 AS cs
WINDOW time_range AS (ORDER BY ts RANGE INTERVAL '1' MINUTE PRECEDING)
ORDER BY sn;
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Utilize ML models and frameworks directly through SQL, making it a breeze to integrate cutting-edge analytics into your application logic with the language you already know.
Unlock the benefits of LLMs and other foundation models in your products by utilizing our state-of-the-art platform. Add generative capabilities simply by calling GENERATE to automate your workloads, equipping your organization with the most up-to-date ML arsenal.
SELECT
DATE_BIN(INTERVAL '1' HOUR, ts, '2000-01-01') AS ts_hour,
GENERATE('GPT-4.0',
array_to_string(ARRAY_AGG(log_msg), ',', '*'),
'{
"system_content": "Summarize system performance using the log messages below."
}'::json
) -> 'choices' -> 'message' ->> 'content' AS system_report
INTO system_reports
FROM system_logs
GROUP BY ts_hour
SystemGPT 11:10 AM
System performance was stable in the last hour, with CPU averaging 50% and peaking at 70%, consistent memory usage at around 70%, moderate disk activity, and expected network activity.
SystemGPT 11:10 AM
Significant performance issues with the system. In the past hour, performance suffered with 85% CPU utilization, 75% memory usage, 30% increased disk activity, and a 20% network activity spike.
Run forecast models on data streams in an online learning context with ease. Use the FORECAST function, get insights on trends and expectations by continuously analyzing and adapting to dynamic data streams instead of depending on historical trends.
SELECT traffic.*,
FORECAST('LSTM', INTERVAL '7' MINUTES, ts, vol)
OVER sliding_window AS prediction
INTO volume_predictions
FROM traffic
WINDOW sliding_window AS (
PARTITION BY service_id
ORDER BY ts RANGE INTERVAL '4' HOUR PRECEDING
)
DETECT anomalies and opportunities as they emerge, not when you run a batch job, using online machine learning to swiftly identify patterns in real-time. Build reliable and extensible detection applications, from network intrusion to fraud use cases.
SELECT e.*,
DETECT('SVM', ts, value)
OVER running_window AS model
INTO log_anomaly_models
FROM normal_log_embeddings AS e
WINDOW running_window AS (
PARTITION BY service
ORDER BY sn
)
Experience the simplicity of creating your own functions and incorporating your existing ML models with the power of UDFs. Expand your applications' functionalities within the comfort of SQL, promoting a smooth and efficient development workflow.
import asyncio, requests, pyarrow
from datafusion import udf, SessionContext
async def query_async(payload):
headers = {"Authorization": f"Bearer {API_TOKEN}"}
return await asyncio.to_thread(requests.post,
API_URL,
headers = headers,
json = payload).json()["prediction"]
async def call_hf(array: pyarrow.Array) -> pyarrow.Array:
coroutines = [query_async(s) for s in array.to_pylist()]
return pyarrow.array(await asyncio.gather(*coroutines,
return_exceptions = True))
ctx = SessionContext()
ctx.register_udf(udf(call_hf, [pyarrow.string()], pyarrow.float64(), "stable"))
import joblib, pyarrow
from datafusion import udf, SessionContext
from sklearn.linear_model import LogisticRegression
class MyModel:
def __init__(self):
pre_model = joblib.load(MODEL_PATH)
self.model = LogisticRegression(C = pre_model.C,
solver = pre_model.solver,
penalty = pre_model.penalty)
def __call__(self, array: pyarrow.Array) -> pyarrow.Array:
return pyarrow.array(self.model.predict(array.to_pylist()))
ctx = SessionContext()
ctx.register_udf(udf(MyModel(), [pyarrow.float64()], pyarrow.string(), "stable"))
Explore, analyze and visualize in real-time using charts, maps, and tables. Continuously improve your AI applications by creating human in the loop (’HITL’) systems.
Effortlessly dive into your real-time data using dynamic, interactive charts. Make annotations, engage in discussions, and pinpoint your discoveries to foster a collaborative and insightful data analysis experience.
Continuously improve your models by actively cooperating with them. Keep yourself in the loop to examine and confirm your model's predictions, resulting in improved accuracy and trustworthiness while capitalizing on the efficiency of machine learning.
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Engage with your online models and effortlessly label your data for enhanced human readability, fostering improved understanding and streamlined data manipulation. Use interactive blocks to unlock the structured data landscape.
Focus on driving insights and delivering value while Synnada handles the complexities of your data infrastructure, keeping your pipelines up and running smoothly.
With a single notebook interface, you can experiment, test, and transition your data applications from development to production. This cohesive environment simplifies workflows and enables an efficient progression from the beginning stages of a project to its final implementation, facilitating a more straightforward development process.
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Experience enhanced control, traceability, and accountability in your data applications with robust versioning mechanisms. Track your code, model, and data versions to maintain the accuracy and consistency of your data, roll back to previous iterations, compare changes, and identify the sources of discrepancies.
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Our platform features embedded model monitoring that detects data drifts and automatically triggers retraining, ensuring your models stay up-to-date and accurate. Deliver AI powered products with the peace of mind knowing that your models will remain relevant and performant, delivering consistently high-quality insights and recommendations in a constantly evolving data landscape.
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Synnada comes with a suite of features that aims to ease the process of building and maintaining intelligent, real-time data applications. Focus on gathering insights, testing your hypothesis, deploy to production with peace of mind.
Explore, analyze and visualize in real-time using charts, maps, and tables. Continuously improve your AI applications by creating human in the loop (’HITL’) systems.
SELECT CLUSTER('SPECTRAL', --clustering technique
ARRAY [txs.*]) --tx attributes
OVER sliding_window AS cluster_info
INTO cluster_stream
FROM tx_stream AS txs
WINDOW sliding_window AS (
PARTITION BY contract_id
ORDER BY ts RANGE INTERVAL '1' DAY PRECEDING
)
Want to work with services we haven't worked with or do want to make your own integrations? Check our public API in order to write these integrations yourselves.
Control who can view, comment, edit and manage notebooks: With Synnada, you can use the power of collaboration to build a real-time data product.
Using a unified notebook interface, streamline your data application's journey from development to production. This integrated environment optimizes workflows, simplifying the entire development process.
"Our platform combines embedded model monitoring with real-time data assessments, auto-updating models with data changes and optimizing resources. This ensures AI-driven insights remain accurate in a changing data landscape
Gain greater control and traceability in your data applications with our robust versioning. Track and manage code, model, and data versions for consistent accuracy, allowing easy rollbacks, change comparisons, and discrepancy identification
Synnada was designed with production-level security in mind. On day one, make sure you implement granular IAM and audit logging on everything you build with Synnada.
Synnada comes with a suite of features that aims to ease the process of building and maintaining intelligent, real-time data applications. Focus on gathering insights, testing your hypothesis, deploy to production with peace of mind.
Explore, analyze and visualize in real-time using charts, maps, and tables. Continuously improve your AI applications by creating human in the loop (’HITL’) systems.
Want to work with services we haven't worked with or do want to make your own integrations? Check our public API in order to write these integrations yourselves.
Control who can view, comment, edit and manage notebooks: With Synnada, you can use the power of collaboration to build a real-time data product.
Using a unified notebook interface, streamline your data application's journey from development to production. This integrated environment optimizes workflows, simplifying the entire development process.
"Our platform combines embedded model monitoring with real-time data assessments, auto-updating models with data changes and optimizing resources. This ensures AI-driven insights remain accurate in a changing data landscape
Gain greater control and traceability in your data applications with our robust versioning. Track and manage code, model, and data versions for consistent accuracy, allowing easy rollbacks, change comparisons, and discrepancy identification
Synnada was designed with production-level security in mind. On day one, make sure you implement granular IAM and audit logging on everything you build with Synnada.
Build and customize your AI-native solutions with ease to meet your unique needs
circle Prototyping is easy, deploying / monitoring is cumbersome
circle Training & inference within a single query
circle ML training is abstracted away
circle Built-in model observability
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circle Higher churn due to data discrepancies
circle Tracking +100M transactions from 20 sources with a variety of cadences
circle Data modelling using Snowflake + dbt
circle Data modeling directly on streams
circle Forgo batch job scheduling complexity
circle Syncronized, observable infrastructure
circle Using several PnP feature stores ($100K+)
circle Most requirements are not met
circle Reduce and control costs
circle Compose your own, custom feature store
circle With a single change, make your existing feature store real-time
circle Wallet behavior shits continually
circle With three queries build a HITL ML app
circle Built-in MLOps features alert ML teams when new wallet clusters emerge
Synnada comes with a suite of features that aims to ease the process of building and maintaining intelligent, real-time data applications. Focus on gathering insights, testing your hypothesis, deploy to production with peace of mind.
Synnada was designed with production-level security in mind. On day one, make sure you implement granular IAM and audit logging on everything you build with Synnada.
Want to work with services we haven't worked with or do want to make your own integrations? Check our public API in order to write these integrations yourselves.
Control who can view, comment, edit and manage notebooks: With Synnada, you can use the power of collaboration to build a real-time data product.
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