Synnada helps you build interactive, collaborative, intelligent, real-time data applications for your mission-critical systems, within minutes and with ease. Powered by SQL and online machine learning.
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The Synnada platform seamlessly integrates with the modern data stack, including warehouses, lakehouses, orchestration, and metrics layer.
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.
SELECT cs.*,
au.user_category
INTO cs_filtered
FROM app.clickstream_2231 AS cs
LEFT JOIN app.users_enriched_5531 AS au
ON cs.user_id = au.user_id AND
au.user_category NOT LIKE 'bot%'
GROUP BY cs.user_id
ORDER BY cs.sn
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.
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.
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Keep a constant eye on the health of both your data and compute components within your data applications. Track dataset-level freshness and any schema changes in real-time, make sure that all data is being populated as expected, and easily verify the accuracy of the values. Gain insight into resource utilization and performance metrics, such as CPU and memory usage and identify potential bottlenecks to optimize compute efficiency.
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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
slot AS ts,
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 (
SELECT DATE_BIN(INTERVAL '1' HOUR, ts, '2000-01-01') AS slot, log_msg
FROM system_logs) AS windowed_logs
GROUP BY slot
ORDER BY ts;
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 s.*,
FORECAST('LSTM',
INTERVAL '7' MINUTES,
ts,
sold_item) OVER sliding_window AS predicted_sku
INTO predicted_sku
FROM unified_sales AS s
WINDOW sliding_window AS (
PARTITION BY unit_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.*,
NOVELTY_DETECTION_MODEL('SVM',
ts,
e.vector) 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.
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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